{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\nTraining a Classifier\n=====================\n\nThis is it. You have seen how to define neural networks, compute loss and make\nupdates to the weights of the network.\n\nNow you might be thinking,\n\nWhat about data?\n----------------\n\nGenerally, when you have to deal with image, text, audio or video data,\nyou can use standard python packages that load data into a numpy array.\nThen you can convert this array into a ``torch.*Tensor``.\n\n-  For images, packages such as Pillow, OpenCV are useful\n-  For audio, packages such as scipy and librosa\n-  For text, either raw Python or Cython based loading, or NLTK and\n   SpaCy are useful\n\nSpecifically for vision, we have created a package called\n``torchvision``, that has data loaders for common datasets such as\nImagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n\nThis provides a huge convenience and avoids writing boilerplate code.\n\nFor this tutorial, we will use the CIFAR10 dataset.\nIt has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\nsize 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n\n.. figure:: /_static/img/cifar10.png\n   :alt: cifar10\n\n   cifar10\n\n\nTraining an image classifier\n----------------------------\n\nWe will do the following steps in order:\n\n1. Load and normalizing the CIFAR10 training and test datasets using\n   ``torchvision``\n2. Define a Convolutional Neural Network\n3. Define a loss function\n4. Train the network on the training data\n5. Test the network on the test data\n\n1. Loading and normalizing CIFAR10\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nUsing ``torchvision``, it’s extremely easy to load CIFAR10.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The output of torchvision datasets are PILImage images of range [0, 1].\nWe transform them to Tensors of normalized range [-1, 1].\n<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n    the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using downloaded and verified file: G:\\datasets\\train_data\\cifar-10-python.tar.gz\n",
            "Extracting G:\\datasets\\train_data\\cifar-10-python.tar.gz to G:\\datasets\\train_data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='G:\\\\datasets\\\\train_data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='G:\\\\datasets\\\\train_data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let us show some of the training images, for fun.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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stGcBvvrOM+dbVoa2hD5PSJmYVIDHrGsoR40mmuKkgjZGzuOZGCMPD/f0/YkcevJwIMbI6TgwTZFjH3k4qWDuz2diDIx9z9QPxAjDkIkxMwy9zrEQGAtMMgwDMYQLAR5jmJ3NlxFiy3o2krEms91teHG7/1MiwKVq1M9BJLJMilmwP5olzyjTj/QLFk/0YmgJpmBov94VWm9JE0zWy1PxtRmny2odmqQauEIXz7RHb34II768//XrGtO7bOM4cTqdVNCVbERj7BNB65ybJ9A6Fru+poKr2meEXP3zI9GV+ZrPaX0f0to/dPz6Ph+/d3G8VDNYHVPTNHE8nXDWYqxhGEa6tmO33Sxm9EWrfoN0oVAsx63noWhomyyRyalev1g+0xSL0F02z9PpzDQFYoiEoKZ3SJmcYIyJKdRkKz23swkjcYZV1PTOxFQggBSJMePMyOQMMViCtzhn6CiWVRWYK1hlZSese5uncyyXd1droGKIv6HNlk7Z1RK55D8sPqA0lT5PiZSrZqrwUYwKUShkobkRMUykGFbKjBCNJ4thSjBG3chDwZn7oWcYB4UzwkCMgXN/YBx7wume6e5XxGnk8P7A0I+c+sThrDBJjgFSIk4jKUykCCEsMfmp3HNMavnFGHT9p4iJEUPGC2Az4jS0xkj1xxRHu4AxKsDbztL6j19gP7ATs+6+K817mWYs+/AzwvvXnfTJxMogNUi/RnYIjxWHvDqHkGdxOTsC68dZcWaRPJvG5DwL8LTaXfOzN/Q0kuLJ6wVUstzJcwI850zf99w/PGg2oveISEl40QgMUzRY5y4deTFGNfFq9iOUBV82t/LP9xfecqHt/TrTvf69QD7PhSFeauTr76ScWIwJxZRrNECIkXPfI8YwToHGN9ze3NC17Src8rLlHMlEUi5Qy4yFQAleo85RFYi62eUyTVUjVKfWqR8ZhpEwRfphIsbE+XRmGgMhRMaxJuZYHY9UNh8xZQPOGAnkZGYBrg40jWiIJjJJxlohxYg1hqG19I3Og4TFOoO3Fmd1TOcI1xqIvso+XJ7tokeoM32ZlmZZlh/Ev5eImLomNAO3DFYZ57FgydM0lbkYmaZFox1H1XLPp54YI/3pyDgMWGtwziPGIpsd4jx9yJxCJobI6XwmhsDD8cDxfCTlSEgjKUemcCKkkfHdrzj94h8Szmfe/+o7+uOZ85A49JpU03qryUc5YKhRJ6UfncVaKdav9mMNibQiOKPC2nuHNYamafAlCa3rOoXyrCmQXsaahPGOrvlTIsDnJgCLX7lOlBqBXR8nryXo+teVjFyEJqyQx/IXy6vUJAlZv7tS/KvmUITphQDKF78lqohdtPwnD1jvuV5Pyk3n+vuFNU7OpoQ2pQLVzFvJE+GXc+Z4PPLmzRustTjvscayv7qibctEqRwh1UQ1UqCHvApNK3rWhYm3soRYa87rPn1sKbDqhzIi63vOl/2nz7t65fGbTwX+Ze9WLViFf73lGeuPCWLidDzTm5HGNQxXYxEWH1Ij8/y8j6+1/kuPWaIXEqoZh5CKA2vkfO4Zx8j5PJBigT+Cas0xJKgWz8oaXQR4oUSQlTM/L9FJUp5XFXHVyMO0rJ1hDLhkwGVSMtgSGmKKJqhDKWU9PHaa1/eXcVwUiKe27uP+G8eR40EZSGOYVDPNAWpYXdRNbhwnQkqEaSyYc2KaVGMeh5FxmghToD9piGh/OjAOPc41tO0GcQ4XM+JbzlPkOEbCFDgeT4QpcDipAM8kkkR9ZSSj0Mc49EzDmTCdSdOJHFUZs8bgRYWvk4iVJaEKBO/0M4wsYatWBbgzgjOCkUTjVbA3LTSN5vm1bcaYEu9etXARjOODuRrPtR9UgKs4ioBZiQDVfQWQkhSjk3TxTleNqE4282gNPjETi5aQjQb+pSpqBVJxspgq7OdzqUkLICmVeZuZ/ysDGQWiUQyrbhh1ws8Tf7H1LoT1vGc8Fk45F5BnrZnXDScRgyOu1lCMkb/39/4e57/1t7DW4ZuOtu34c//MP8vr119gjSZ55BQZzkdiDHRdx6bb4Jxjs92UJJAqCCFGJcuy1mh0C2ssfhmtRfTLxSp+uryfQhXLJ8VOyenCDqMIK5VRBWvNj2Kis2Ynprp55rjaQjPDkOnPE9MUeP/uG87nkZ/85EtyFg6HB8YnHv+8vMzaweNnqVq4pllnnUhMOTNlmELk4XBinCbefPeW+/sH+n7k4eFIShljnEIjztP4RgW1V4jHO4dzHiOCdQ5jDI33WOdIMWFlIid15MUUF5lKJkxqZU6jOg+dswx9wFqDdxZnDU3j2W03WGvoOovHguTiZFxAxUsXUB2l2i354oC1kjT3Ys788uvvwPwDUgoM45GYJsJwJIaBaRzpjyfF/ceRECNTEeA1JLTix7rZRcZ+IMXI+XRkGnu2u1tuXn6Fa1t2r1/hthvujz3vHk5M48TDuwemceR07jn3PdYZml2DdZbdTUO79YQQSGnAMHDdTWTR0M2cE84J13vBu0zbCk3jyDExTXqMCmhLFksyHsgYExHJeCN4q1CJcw4xBldgLWME6zIiqYiHuikbkhhGB5GPa/810MAX4bRooInHAMSFzieyBN6XufPY5FuJ+1k7qllVmazCm7xysDzOuCsnzouQuRDu9TepPysTeoaF5pPPAmk9zSt+hxQhvjp/7YG13aAC3JCNuRjgnDPv373jmzdvsNbj2w2bzZaXn3/Fdn+jGoQ1xBg43t8TppHdbkeMiaZpaNoWWWl5qimlWQnOZXJdamLrBykj9QRPXh+7sjjmx7r44xknoSx9RB3Huknm4oXIGu8ruuiqX2VtB4WQGMfI3f2Rh/sj+/2Ow/HM6dyT4qNxv7iv59vsPclVA9dvxKTa9zQl+n5iGCcOx577+yN9P3B3p2x93nc452gbwZpmPicYjHE462ZiLjEGax3WWH2ulEiSMEk32prkk3NNsy+bWFYHafVnhCLAcxaaJpIRUhSSkXmf0hT8C8m9Et0rQ1Sq0H5uYy59lOF4PPHdt28JceI83JPixNjfE6czQ99zvH9QSKRE1IRpUgGeVYDnEhaaU1bH4DiSYqQ/H5nGgf11T8wdfrMhdp4mT9zfn3j3/sA4TNy/ec84TPTnnn4YcI1nE7e4xuE6wbW2RGVFIOJtwviElYwxicYZbveZpsl0G0PXGWLMTINaPK5Ak0kMoUBgGmGT8NbQFHjEOqPj6AzWldyLOWO+WHoF4U0iBJE/HQLciMH6BrE6iaVoqkpixawNqna31gxW+HVWDXwND9R/s6pni9MuJpCMr5ByDBATkhMuBUSgaVuccyQRsggpw5jybMaLGIxYrPU6fa0ht23ZYT2IAb+F8kzUJ3hiY64w9VlIlfdy5cGo966jm0oiQXpkYokIV9fXGO8R4zCuUSKhmxv2+ysNa5om+mHiZz//Bfd3d+z3e66u9my3W74aA23bzvdiraFrVYh0XYNzDmMSxliqqJkhIJiF3vKMq8+qhlGeK1OiLvpzCbcKczijFOy9aTRsTrMoG0REHUpFglQncRVeaXYyV2GqC1+HTbCuxSVL2+0JwTEF4VffvGHoT0808LrRK5Qjy1wqZnO1ETR8OBFi5nQamELk2A+czgPDNHH/8MA0Be7vHzidzup8jEV7z4achJQghmJVNEWAiykCfCEjyympk1N3UyRr2KggYKpJn8nZzuNQjT7JkCKci5/j3E8MQyg4bIN3VjV0r2n4bdNgrcFawdoLe2i1P6/c/0+Vb0A3kz/5xc/4r/7oF4QYGMYzOSc2HhqLhs8lVRRqEpN1js5YUo6YUTF9CRBywHtfFA3h9rMXWAtX1y/4/PMf4ZqG5rrDto6uGWmc0J8zZhjpbQ/TkXA+Y4IlHE+k3vAuvOXhncUO73DnIyYMiHoncc7QOEfXOq5v9nSdo9sJ3cYQp8B47nXuRVW6sgjJ2BkKM5KxWJw4lWfFDyXGlPBjWWIpq59Pyryrv39k+2GdmMbQ+BaxzWye1weFZeLUMKP59/LZzFC2giIeRyekqXiKYyBH1So9xTofJ9I4YlLETSPGwM5aWu8JRhiNIWbV4NQ9aRBRU7dpXGFVaxGiCh9XshttA8ZdWAbLlM/M+PsTrZ9ZOKl3u/xExWJVG4FgLkdYRLi5ueHmxUsyhiyOpm25vX3B1fUN59OJcQyczyN/+Ec/41e//Jrrqyuurq+5vr4GcWy3mznVt+sabm6UFApjaMVgs1HAQKpYW2vLVUN7zvxeNqicVUuPMXA8HhnHgWEY6Pthdr5aa9nvrxARvFez04i5SEOuEE4qmORyP9VKyQslLQbnGzKObntNomMMka9/+R3T2DOOjyGUteW0/Oh5l7R3xa8Vv/3u3T3nfuTd+wfe3z0wTROHw5Ewb1CxKCVqUudkyGJIUQihboJq5VhjcSVL1s5w1uJgrn1rjS2w3XrylM+sCuVUHIMxRU6nkWEYcM5yaLS/21b7u209XaeKy9WVOt3axoJd/DDLLM5PDKkF7FvdSkr87Gc/4x//9JeEmBingBHhy1c33F5taRvPbrNBBCKZLIKzFt85UoneqBEdKYL1jm5/hfOO25s9213H7c01X33xGmsN0Uxkiew6x8YbTsdMOPQczYlwfGDMB9IkhMmSgIf3EyFHtmbgxh1xOWBDQHLCiKPxLZuu4fbFDdtdQ7cT2i3EaWI4WlKMhCGQQiQbIRsV0t4WXDt5JGmtg2wKo6GwhFxW018WdODCWP3I9oNr4N45rGsuBLR69dcCHGaoYf724nCZl9wMARSBkjKRjETNrhLJkBNpUprWcDgSTidsCRdy1pC7DXhPlkS0mViEQUwZI8VsF919Uxak8IKLScoKJwImqSael5m+RLzMCDyzwKFoYSWiggwhlWSOFOeg/7qQY3++0HpEhLbr8G2nHnnb4JuW3XZL2zZM47gc13Zstzv2V9fc3t6y2+3xvkGM5XQ4cTyd6NqGKYw0jVc6UqNRDGKMRuA8mWCPQ9MW0cfKCadQkyElOycNKYuiLVihVSesUyIq7xzeaUTNpQBfuE/0tfZnIiWZsxBTBsRgnMGSaNqWmAwpDsRpnDlUPtgUz1uENwqTaMJNZhwiwxg4ngf6fmQYJ0KIhFgBMMEap/j27JQ0K+vCz8/8JDNxfRspzWnza1+QGClWDRVZBJi1d30v6rwtoWs5K0YvonPKWCmbHTgfcd5pViiOXBNxSGg0zJKMs0521Jt8qoqnbIjZEBOEUPtgQ9Nc0XiHdy0ITJNi4DkVRSUkjseBKUwMfa8bT9Mw0uIb2OwMbW4wrmW33+K9BRNAEs5mnA2cNpbhtOO4BYkTkgIhQl/CL/MYiWEEG8hSkoGi9nNqDdZ3mKbFNlfYpiVJZIyJlKyuYImIGRAbQBqwHjCqjQNGPIjSZCfsAq/OcKEOWsqLdSfZkKTS135c+40CXET+XeB/CHyTc/7ny3svgf8A+H3gj4B/Pef87qOvWpp3jpvdFb7Zskg6mTXuRQistLvy3fVUSRffXTonp0yYVBORk6appmlifDiQxpH7P/klh2++ozWGG+9ovGeToU2JyQgnq1jzgG4A1jRY0R13DNW8DeQcSSRSCgUiKDGkSaMfikpduMBjcdblQoOhGZEx1TjfkoobNXMspMgUgj5LEeTXu5bbfbfyiwqvXr3i5WevadqO7e4K5xtevPiMzW5Hjol3b9/hnOOrH/2Y6+sbvvzyS7766qsSteKIMfEP/9Ef8Y/+0X9F0zh2V1u6zYa/+Bf/Wb748nO2XcteTAmPktlzfpGEQd1El81UZGHJ04FxWDuVAgz6mS8p723bFg18z2azKTwmO0SkCNs0hzumrLjowkURSdkQYla2SMoG6zxdu9HsPLtjNyUe3r/l3XeHmbfiSauODeZVR85CTJlzH5hC4uHhrFjrGHj37sA4TkxTZAoaauZsg7PKX67PX0i1jAr1KmQrTOJKFINkvf+1GZNjJIawOMREicvmMMqiOCz97XDWE6Vu/AbvGzIwhcCpHxZ8OSec9zTe4xvHaehp2obdtmW7bbFG8E43Be9Qh7gRKs+jxmw/twkKYjcY/wLK3LXGsbv6s3z+xRc4IzQWQgwcvvuW0/mkWHQODEPPN9+8UZrg04Fzf8K1W7qbQLvZEu0Lst/wub/hq598yXbTsGsU4x+ne4bpjtP5gZ//xHA8PfDzn77nV7+443Sa+PbbI30f+ObbI3fHM86qMhdz4tgHCBPdvqO5fk273+Nvfg+33XLqj/QPZ0yOOCYMCeMPGNeTaEjSAYYpCSQw0mCt9nmc9bjqgM9zDsI4TYQUZ1gW0xIfUzH+mvYxGvj/DvjfAP/+6r2/AvynOee/JiJ/pfz973zcJZcmolieXat0Kxikmt/PTY/Zhymr/aqo5DM8K5ka45pRZ0VOgTgOhH6gPxw4vb8jW8u265C2IfQ9YRiYDIxWiEIZLsgmg1FHQ46Vjzlo8kGKhDQV55HGmuaYSjpvRsprLk4TQYP3AULQjMGYFKtOKTNUAR4j4yMB3tkb2LfUUa4x37vdjrbt2F1d4ZwvGKfDGpkjPLqugww3Nze8evWKmlI/jCN9P/D+/R2ucfRhZDMMHE9nhnHCWauJI0Z9DoXDjTn4c47/W8ZmHW5YB7SGv1XC/rUGXrVvhQD0vVqcQQWVlAxYQVIuG4csG0euYY/F1ska3yTGYhCcV1BYrFGt87EDk2Xd6DMtwrvAz8p7ESLDOHE89QpN9QPjGDSUr2hTNQTQ++pDUB9JFb41hE+K03WOuy8deDHn68XXFh01pHDlsFcn0tLHK6GuTmozP0OMkVAzBJNadyEl2mEkQnG4mYKDmxUmT1FE6r3VW3y6SkUcYjrEpLIWHd5f0XU3GBKWSEwjMRrGUS2NlCJ9P3E49JzPJ06nM6fzCdcJox1oo+XUR4ZJNfymaei6hl3X0pRxDTGy6TLnYUfXRU73A8NxxFnD6WHA5ERjMo6ERRUrksJiKWUwDttusc0O468QvyWeM/2UsJIwxoNocpX2eINIt4LYBJGGXBzU1TrJBVOtVmRCE7imANY6svFIdv9kNfCc8/9dRH7/0dt/GfhXyu//HvA3+S0E+PFwxx/+o/8PFA/urMnNMb1pnsxPk12SLpZck2zqHldvXP9JqWZvRU1pDZFw6onjxLu7b7k7vGXfteSNxafM229+jtx9wykF7sI4pzODZjE664qTr8EaIRYuhFr6DJjLQpGUXIicMbHAJClolpaAd6qtqrauWWfjNBJT1oy8rCm7U4mXVUwQbjYWeMGjR6XGg799d4cxhpevXrPd7fju2+948913DMPA+XwmTBpG+PrzzxjHiTdv3s7wTOVueDgkxhD41TffkgVe3t7gxOCdIXlbnDWrsl/FEaWkP7Vc2soyYrG5U0qcz6dibaSS/Wk4n88YYzgcDrNW7pxHkLn81ww6zQOtWLFWoRGMdQWjToSYiNPAcJyIKXM8jAxD4P7td7z99htymrBMT5bLGuVXgZcZgmr650HPcTideThoRuUwKM7tnKf1Tp3zRUOuRRastTTeskT7lCsVIW7LZiVlgQsLWZSez1Kd+nVeSxYuCjoUzV3PrdDfNGnKfT+cOfcDISpvRy6hgMY5jHNYrwkxmtKvc+18PuO9Ybdtcc6QckPXWI1ccXbZuNOy/pZOFFx7Q3d9Q5MM2xuPs47rz76gvb7h/v23fPOLP+R8PvInv/gZ94d7rGgcdZgG3t19yzj2jOPAFAZCH4l3HcOp55c/+5pwGunMmd/70nC1a3mx29E1HmsDzk30w8D9d5HTOTMeMnnS2O7Waz3ZV7eZzpdotqJs9dIQo+P21Zf85M/8JXzbYZsrQrIc+4F390c61+L3Hd5A021p/QjSkc2ejCEFS06CKxZ9BiJR51Lla4lKRqab5oFx6hHT0jQ7MJYg3TPesefbb4uBf5Fz/lrHL38tIp//Nic5nx548+5PlHVtBQfUskoqsAo2nOrCXXg8UqpCMc2Le72w1yvTWItYp/NtSqQYefvwhrvTe86yo2GPI3H33XvOYeQ09tydjkCmEVMyqgTnFbff7TY4awjDSJwCkOfENt9orKmm16vWZFNJIY6BHCesEdrGlAwuFRkhFuaynOkjhAwhwVQ5hEs5rfDZ7YwB11ZN2dPpxK+++VaPi5Hr6xvevvmOd2/fqrAZdfE2bcurV684nU68ffvuAppIUyRMA+M08e13b1R4pszNfk/jHZI9zi4CfB23O46jMqwV7oeaKFRj2SmCfSbzWWluSwm4tTN6NZ5cQjQ1y7RrW9qmVQHabcFouFeMiX6M3J8mphC5e3+k70cO797w7u0brCSudwb/eBUUrLleOyalCQ0h0Q8azXM6q3USQmIYFYrx3tB4jeJoirYds0JjzlVe8svLzHPerqNOslp7WfU2MQa7dAGgTm5BZm6NGV0VjSGu0MoUpiLAB879uRA6BRWw3mvEiXNY5xGBscBAw5A0+aRx5KwkSzUyJWeFUYQC+Twaxzperr2ivdpjxNH6Ld459i/3NPuO4e0v+dnXP+NwuOPnP/8jHh7e03hL2zpiDPTnO2KciCnoBhUT09Ri7MC37lf0Dz3bZuCnP7Hsdy2H61s2bcu2s+y2lmEYObyLnM+J8ZTJIWNyonUBywTXmV2nafHjkFT7zp4QhOuXn/PVj/8CGMd5UGqD8/DA/YMhdg3X2xu8szTdju0mIGaLuFtytsRgScnQtpa2c2QySYJiAAFSVDhw7CPTFDkc3wJHjNnimxsQIaYR8scFEv7/3YkpIn8A/AGo2b5uYRp5uH+jkQBFiM2lwViytypjGytBME/cKsAvr7lEshR12LoG4xrVqCZd3DFnsjUka0hWiEYIOTHFwDiO9P0ZUiYW7Dc2Bp8sOTm2G6emlska01n1NhGcKyFYuTKxafqyFPMqUUxRI7PWJaLmvvEWkpaeSglsWtUurAvePHV4SSnoIOjGl2Lm4f5eM9EOD+oomq2FzMP9HV9//TXDMHB3957j8QSS2W47tQTiiDHC+XjkHZlt4zm+vCW2DV624C1hGgmjsrQdjg/KgVzSniueP2+2eb3JPhbgmaqNrl9hLcCXsa2CzpZ46e1mw3azpes2vHr9Bc57+r6nH0aO/cjd3VHD/A49wzgxhR5rNd35sfpdrb1KJpUSTDEyjoEpRE0KOQ+cTj39edB0+F79FpISeZqwxjB63VwqW55zjtA2pdD0ulapztFUYJZkLMkUp+68SefZYahWXvnuE9tBO0vD8rSPwzgRYmAaR6Zx0CNS0TRygizkmIiEle+pOIZFLcPea+Zo43XeerfEPFcW7kq7um4xwRSU48PZhM2REEdCEGIcyTkUWFN/Ks9ITsWSLdaVLmFBTMSaiRyPhBFOR8N33244H1vScGLbNnStY7dxDMPAm+/u6Pued+8O3N0dmKaBw/FMilNhdbRFEbKIAeeUvdEY0VBGBFfqlF/tGqYXO1rvaTvd9K1NiASMzRhXkrvEYbJBnFnVxy3hnUYwGVISkIQYwTlbNkeAscz7j9W/f3sB/isR+apo318B33zowJzzXwf+OsCPfvSjizE+nR742U//PufTcZ7Uzlp800DOTIWacSFeWmJey8nLCi8Vr4uZaazBes12s1ZNQ99s8c2OlIRxEmKCISfoGlLnmVpHNEJ/CpzDwOF05O7NW1JKeFGC/Gbj6DaOTddyvXeIa2i8mktI5UcRDSe0tsS5RiSDyxrBkUImTqV0ppPCo1A4FJIle1PMLJAkxAK/aEq4CkTr3BPBU52BIsI4aMjYw8N9wc5Vi0gpMRS+4j/6o3/M4XCvZD99T4gBI4kvXr9knEYOpyMxRL755de6+M9H9huNbDGvX7HpOt6++Y63b77jcHjg5z/7Y/rzmRDDzLo2FVM9TFPhu07FcbaMp0IAhaN5tRGvBfdaUzclscUYmWGW66sbbq5vePXZZ/wL/8K/xG6/5/544nTueXf/wM9/8SvlaS7WgDca01sZ4B63mNV5HJJaQX0/cXfsGYeJb799x8PhxP3dkbdvH4hTZOxHUkg4a0p2nuLHawvJN8qGaKzFt81lQQ1raJp2xv6ddRhracrzWWuwxsxWDXkp+qt+gwJBzjj9NG+gx+OBEAKnwz3n/lwSShTmIYgqKEEFOFASSnLZeCLOW8IQcN4yTZHtrqVtHLtdq2NgdSqO8anGOE2J0ynhXEBMTxbDeQgczyeG8YGcR3IeSXEkhgnJAbLCpwYpGauuOMwN1kyIieRpoD8Yvv36G/7Lv/0rus7x8nbDpnM0zmkY8JS5fx8Zx8S79/e8v3sgxpFxOgCJ671h03ly1g3CitBuPD5bjDOEeMaLZ9t6jBE2/oovP2vJJEgBIeL9gJgz1nX41oJ4LC25COzCJwm5bHha4B6TI2IixiS6jUekVYEuDwX/blGh/5vbbyvA/2Pg3wL+Wnn9j36bk6jJPdAP51mris7PntpZgK+Ili6cYmVR1/QKk1WLyWKRxOy4kYqFJw0zi7EEh2R04zCmxHIunM0pF/rHGHWRATZAjGq2VseEUPkMDK6QgxuvGXTKSqZasc3q/ItSnEICtmg0zoGxCrcoRzRETQ7TJKWqgQedGMY9LXw6CwOzOK+00ofib6aEjRi9PNM0cjjck/PCRS0CbetBMs3omMikGBiHnnEYGIcB7+ycfDMMA8fjkYeHB96+fcv5dJqjaTQGOZCLyZ4KCX4s2tVSZqrCYjzapFeO6t8gwMMUSoV5Tz/0NG0704fGEAhTgbmK0HMl9lnISArMnAmra+kcKGMxV1hZnL3DMDL0AzEUAR4TQXTZ1egaKV54EfWVkDPGWnJO2BJNYozB2CXWvVI02JyL89kUISYlrLTOvUJxkMsxq3uvgj6WcQphKlZZwFDqtgLkpN9P1d+k+KyORSRl9WdYZ3DR0rQeY9UiaFpXYBTBiqy78KIvl/BPvc+cq4YdEFMr0lcopuL+xSJj4QjRjFS1X8laXGEc4PBgmUaLtwNhUgE+OYVCjkfDNGVOp57TeSDFqYRQZkJnSckWiWLLXCuO3pSYph4hlWxZQ9cKbet1PYxlLE1CRM9nCp5YLexq3Wgr41MpC4p1o1XoUwlmiGRGNMfB809MgIvI/wF1WH4mIj8H/peo4P4PReTfBv4Y+Nc+6mqPmrEO3+1okjqfahaaauBg3cS6LqDe0HxfcxibLYJLsWeH2FVSTfXWYxEc05Tp40iOCSea5NE6XwLwhU3jIDaMZ0/rLdEsWW/Oe5zzWKdxvEa0dNcYAl23YXf1Uj/b7BDfKFRQNHCbS0BMVDzZGEPXasLFduvpOkeImfMYCClzGBJDULM0IUWzUg18v2kXAPVR22y2/OQnP55pOHPRcDWWPRZHlnrPkVrBXjlhNq3FmZa2MXgLMQQaA8N+y9VuQ5wmxr7n8HBgGkfevHnLr775hrv37/mTP/k5p+NxNsNr5uuSTbqKc+cSEqvCuTLWXRpY1Wmdi7lrLoi5QDifz9zd3RNj5u7uTjVZY7nabcqGpufebDvN6GssXesY+4Ff/uxPOB2PF32ofV5S46NucH2vgvvdu/e8e3fP/d2Bt9/daSWVIsAlaVZvja4S0exCY9VM7jYbTCm+bGuse6PJNN12o9an94W9zpImxdPbrkNqQYfiLzLiAKMVXMoaCVMoiV8qeKdp5Hx8UKto7CFOiGRsNgiJNGoCTZgS06TfGYZAjAojhhCwztLsPNZZrm92bHct+/2W6fUNjXfsdx1NiR1/FDqDpcfLhBehEaExhs5u2PiGfRu43Xl8bnjTecbW45zBWavUseNAqrVmxWCl+hVgGnumOHJ6yLwxQttafBrIO0tuHa5riEk0ZjtDSCemeAYM1u8QMcTcMI6OGAfCpERXevuRb7/5Fu/+Lvtty5/5yQt224arqy27Xcc4Rg4H3RBtjghJYVRRbpwkFsHAXHSiRjRBzL2SaOWBnB/IeSLkO0LUEMqcJzKebEoM+Ue0j4lC+Tc+8NG/+lFX+DVNjMH6Da5FoQ4xOOvxXsNvrJ2KxrEs5KrRmIIhWqNEPWJ0ojuvAlzsomWQs0IXIZNS0PPFPIeqeWvV9DXQOEvyjtbb2VFX4R2lj3SanCEWMMXBooRc3WaPbzrM/hZpN8pMV65vEpiclT86a9zvdtPgneHmesNu1zKFxLEfmWKm6QPnMZaQluITiJo0ZGKPhPOzfdq2LZ999tmymLJWJAnFmXU8n4pA6un7vjj7Co7ulR4zBo31jjFiBcapY9O2pBgIk0aLxBh4eHjg/fs73r97x3dv3nA6Hkoati3+iTrOS4LVLNCrvV9vs2KoT6CTSw0cFtsjRrUuhn7g4I50mw3H45Htdst2t6crceW+4NE3N1e0XUPTONrOcXg48O7bb58K8FytsFzizTWjcRhGjscj9/f33N0deP/+Tjm9+6mEjCrTXtUcRVSpcN7hvGcYBo1GKQLcNw3dpsNatayccyVjWEMpybFAKoZkZLZsRCf+DJlUzXwYhtlizSUCRSkLAjGqY0znX0RYslunMTCcR0JIHE8F158mzZ60Bnd0WGcZxjPbU8c4DnQbR9s2NCXq5rl4eiMTTgacUH6ExkBrofOJXWchOLrG0TYa3eWs8pPESZj/y4qFO6twylg4wfuzwoxda7jpPB6DS45oJmI2pKxViiI9IY8YaXCuxYgj544QHdNoGPriNDQBkcz93T1GJm6vO754NbFpNnQ+8+La0w/VsokQksqRAvHkOfysUIFQfVsUX10kMQA9cCAzktJ7Yj6qHyBGsrRkefUEIv1Q+0EzMZt2w2df/IhhnLDWazB7yVAjlwWa0yIMpEQqsMAEZtZ2ZF4Yom+ScyJMAykG+tOZcTzN/hspFJDOuhIhYKmMnpV7REPB8ioUzMzEUN45vHdMIWjKdFTvPTbR2UbxdhZGcxXezD/eGpptg/OGZreh27WYKRJkwISETyNTLqRDelNqYqZ61uLcmlu+fJWabi3YrFwXMSWMFUKMtI2nbRql7BwVo1bsWjWxGkmy3+0JQblSNpttyZLUsfK+oes2tG2nBRNqSbbiYKtuLYUSKuNg+bvSoFapLeWPi8fSP6RGFOXF2annKdHoRjC2zh1NTNpuOnb7PTFFNtMGESkauNMyeHNo46Mm1S9hsFaRTN942rYjJY2j32w2nA491bNYuS0kGxY+coWKTFyccDGE2eFuKqyRos49UQFOapHckKxFciJaq1DKnKyjcz2VkEEtCqFjNfRKwapZv3l2WteUdEqhgZqLECa10oZhpD8NmvJeNPBQGACzGEwErd1ZchrqNKs+qNVmvR65/Vb48qXFOdhshcYJN3vDfiOkvef88orWC1f7lvNZRZESki0VpLRMWiRPmvgmaDSWJlUrA+NI5nCIkARLy9VGy620NmIN7JrA1EV1OJoJoQjdbLEy4Iz2j9bl0MkVx8TQB+7vT+SUaFuP96Zka0akUF6IOIWGYiZLKe5QV21WhYs0kXNknA5M05EYJ8bxRIwBYyxNs0EwGPGk7DjHhvB0P3y2/aACfLe/4Z/+Z16SMFjjMaKORyNWNc5iGgK6xo0UnohKN6tiQsrCVnN1IdiNMXA8vGMcz8T8Dff3J4KmVGIRfOPxTjWJrm2o6cIpB5CM82qG1ZjexmkcdOMtXddqmnqvhPPWT5yGiVYatm5Lu3upaePGzfcPuRBvZbwzbPYe7wz76w37bcs0BUxzZgyRnjNBJuViSRXqmNQ7LuMHenRVWxDBN24WrBr+lUlxVziYR4ZSx28oIYChwC7VQlkvTo2rLkJJw27YbHbc3NwyTSNN2+AGN0dXkBcsuyomuUB/c6RJViGfqlD4gBDPxam1ho1U0dFFPqffe0/btWy6jtsXN7x48WI5uAYECKXg7FjM3ke+BCibtAWj7HEZw/WVJuVcX98wTYnDw3mBdUp5PUPGGKthcONU0t+ZBd9Yoo6GcdApai22hBdOY69QxHZD3irUEivGPw5FY9dN1Fhb0t1hGAdOZy10MJx7YkyleIMhTKNWRI8BEU2Hz2TSpMLvfDwxTRP9eeB07IkpMU5ZcX8KE302YCMpW3Lcquqgav/yU+fKuh8FPn9h+fylzr221b68vfLsNpat27Gxn/PwsOWbX31NGE+ch4HT0CPVz2OMFgIeFfoZRp0n3iSsQJ4ywzQRhsx3aeLBJ1ze8voanAXvC+68C7SiORQxnSEbYjAlGiSQ7aD3bFX+kGDoI5JHfvn1HXebIyGOjOMR5yxdo2gB2QEdOXtSyCQiIWRiFsg9pJ6cJsJ0T0oT/XCgn04LlCiCc1vadod3O9r2BSEK374PhOHjIlF+WC4UY3DSkDE41xaGP03KyFmjAdY4qIiSqM/Zd4UcqQpw42xxYlI0qQkxHpGJmbt5BtGZtWpZZYJW52UtqWUMBTYxqok1zUy6ZAosoNp3YBwnxE4zHlhJ+auqqXdZHBnWlAQKi/Ue5706r6zFpkK2XyTZxdqQapz9hlacP+ok00gGKFmkOZfyVZkokVCeVfsmztbOGr8TE1ZKll5feaZ9yTQs6fLlFgtyPf+t518xFlYNvGrYsKjneb4ENd798RNXzb1mGa6f0xQYxzqlYK3jWy2ClBKh+kee7Trt48WHouesfC2a0GUxqqLrd1KGqK8pyoLvV8dk0XxBWS7XXSloNq5qzZ4pBGzh/DY2IzGQRbBFaFfoJOfiHyn3mwutLsXpV1TKpYcrNFQgFuXaXjk6U9bK6xkwtjhjhcZr4eSq6DSNcrg4W/lNnu9HbzX71VqhadVybZzBWSUq67qGEFr2uw37/YYsmX4aqqE1T4Jc14wktVSMkkZVJ7gxuVg9qVjPscCr6jBsfSS29ZlLRSMxSpJl4kwKJrbKBN0ArFkcwtOoEFqKVivtGEOO6rwVE0h5IGEJQfczUg/pTEpBC0WkiRCVDlcXhCqi1rY4180/amWd+NhQwh9UgIcp8NCrmbbfKxeAUltqmnjMUiIB1AFnxCDOKXyyEi5V0IgG95Yag5EQDOc+0feRMUAWJejRxJKIdULbqqNIC5BGjscTh8ORcZwwRsO6bkoJrqv9luv9TjWKRnHLvu95+/497WlkGIWu29FdfaZhYc0Wv7kCMeoiycqgBxFvHE1raRvPduO52jUMNhOHjKQIoScMZyUCKmNpKfHkj1noYHbWqgwsgq0INWct3ulQJ1OLG5fq2CkxDqNmLdZon0zNCS9WDs9inE3TsN/vOJ+VllZjzceZHrZq6pXiwADZrAW04ps6jIujenFcwuPq8XPSSkbRCjLee7pNx2bT0ZUf6+zFeWt/6ALNeNcWqtqn5U+KnkpKWiS3Rmhoarwy9+32O17c3pJjxotuFP3xgeH4gJDozyVaIgVSyATsCrpbFA3JKgT685nJGHLSwgbOebrNRilWMTRYjMtENFY5YzFYxCkUl1PCtYp/WyNYATv2jHHSeP2pJ8QJSUk5YKLGtI/jSH/uOZ3Pau2X2bTZb+i2Ddv9jq9+8hVd1/Hisxfsr3Z0rWd/tcVZQ+s91ooSSq33JYHGG7adbqSbrsEYpWk1CF3b4F86truOv/iX/gI/+vEX/PRnv+Af/uOfMvShhGZGSIqfNz5zvc14l9l3QtcIMQlj0Iu6Anu1XcCaE85mNj5iLHRN5sUVZCZSnshZiFE18BppBGaupqPQrClMjbrxpClw9+6Ec8Jw0qzfGB0pWcQcEXdHBqakxalTGIhh0HlTlo51Hd5tsa6jaW8xxuPcHmOVhM64hjRFMAOUsM7f1H5QAa7CI5IwbDcFXxNT4lqVN1myQIxqRIsghbZxZiKcBXjVOqRgUbkMcGYYEyFBFgsSywLNRcPQ84YQiUkTUcZxnClArXVsN1u22y0313te3l5BToQSuzqFifO51wQE7hjHwHA6EoYeEYe0WdkJKRqgEkmTC8buvKFpLE1jydHgDDjJkCbSNJCylAkms9b/65pUwSgLK13lGdFPmK0LSnSP0p7WAq0Fz0zMG2O5eeYTlOaso20VSmqKZaJOtrjcy0qTRpaE+nrOhfdGyKY6K2eQ+xGcwuwMnbV7FkIsXywZ591sDcyXynnxmxijhFJyWfR5ua3iPKXcT3W6wgzXtG3DZrOBlGltSdCJE7E/EU2NQEhaPkw0rj+agKQSFVXhoqKlh2kiyuLs9T5hnMdlcE3CFqQiaYQ0ynxnwKpGmzNYV/B1FArMgPWNzrYwKvNd0o1FaRumGaKYxkmfcLZMN7SdY7/f8Pr1S7a7LS9e3rK/2uGcUtGKESp33uP+BhWqjdP13JasT1M4xp1zNF6ZGb/44jOurnYcTif++Oe/JAWBrAWEK/uls7DtMq2Hmx1sO5iiFE6UOtEE7xLGjBiT8TbiXEZstcJES6plISUtbpGyIa/itJfHUOurhjenGOlPEeeEHHWcQrTEbBHTI+4EZKY0knIgThNhHBAMVlpEHJttgzcNzm3p2hdY22LtDmO7wnCIogY87csPtR++qHFxrhwf3jGc7mnbjnG7w1iH9TuM9YXJr9RBUXISqqE7v84LUWEP31isbXj9+jNCuGEabpmGzxmHM3dvvmEah7kwVoyB9yctgBpixrlWy025Bu8dL168pOtaXt5e89nLa3JODMORaRxpfDObs7Vpkd0S51zuqZLIKjyvYVE2aWz5dDhzGgf644nDt2/ox5Hp3Otzi/JUQNFGBU3AeNyXtWxa1gw8YOE2yWkukVa5YaYwzYLQGgu27PnVTJ9reOrPIszqyJXaf87NzszNZpjjw9eUCIvAXmnZ5Z/HdADzcXOHPvN5/aR815XQvKZtlVXP+5kUK8Y411nsh2GOYQ9T5Hg4MI6X/oRMlataUX6KifN54nA4Mwwq8LTSjbInppA0ESaF0sd5xtGzXdgGBYVRmFkZteTZZtMtAh3Bes3wxTqsa7He49stTbfFNQ2JotyIxeAKju5nuE01vqgONGPYhpGgRTLhLKp0DGelVBVBjC2FS/I8b8vCpO0c3caz3bZsNy1d52gaW9Lvi5JQfDrPWYXWmlJY28zMm3VMc3GIGAPbXYfzlhe3t7x+9RkP/sjd2zNDDHirFLH7jfD6haVrMrsu0/lISJYhlGKI4oDMbpexTjOZwaj1tbL6lBoXkEQWxfirk9y7WGLrKc5UW3wHRgulGI12S7lad4LG4zd4v1FMu2g+qdWoEhGLkQ4RS9fd0rTXOLeh29woKZZpkOLzS+gYrCHd39R+WAGeM5ZICpH7w1umsWez3XJ1fY3zLfvrz2naLTMemaHGvElJSaY6t6SqamqiKgtfy2evXijnsmSc0YKov/jZP+Z8OnD39i0Pd3f0/cS3374pySCWtt2w31/x4vYFjfdcXe1oGs/nr2754vULYpw4Hu/o+7Oy+5VW5cucSJFSwTtVgCc0lEpNXINPgguZ6e7IQxw4PRx4+8tvGKfASEPGIo3gS7WcOZzOGB4vl2ryzdl6LHhnjIpxUp0nFG7xcsO1Th9QvN+5EPGtk2qWtPfZJ2HUEdx27Vz5p+/PK0G7kC/Vf6tm+1irrtcgM3OiP/5s7Qu5yHL0XkMHNxoRo7woKsDrhjKOI7/65hsOh4Nydk+aXt73/ZN5mUtptHGc6MeJw2Hg3bt7xnHifB6YJl2Ym82WaQychnvCOM3JOiK6qShlsMy6RarRC15juDWOWulyFctWDTsjYBuF4JqGbnvFZrcHMUQcKRvAk8Xhmo52s6XmQyCQw6R8O7HDOKuUFKjAHs5nwvmMJnQaKMUGYtn4x/FMJiHyku22YbdruLrq2O42bLcqxDWYQK81k7U9kTlK/tZ2zeLYlurTKHMsq7J1fb0D4P7uxLu3Z9627/nFT3/FMU50TWTbwYsr+L3PHZsu09oBZyZChjF77S+j1bA2zYT3k/YFdoEDpXgLROe/MapQGXEl69PSNgqrxgKDaGLPAFjEthjTkrJFiyhZNOlGcey2udHN2tnFJ2OsCnDTIeLw/hrv9xjr8c0GEUNiyZWoSW2/ycpetx+2qHGZNLEw9OW8EjZilMwmaMUU1V5FcW4RNR8LfWMqqq31Gsmy7Px2rjTiLXirDpDb21vaxjGczhweHjQqo3CFOLdVPmXvadu2mOZNId/XeFiRTNM05JxoG0/jHW3j2W+3tIXHusaY56LC5hkMK+p6DKS+J1lDDCcknkn9GQkRk/IMf2QpNSqRWauXZ9Le1iRQVbjVdOpzX54zxTmkzFpNJEGMWjtGCmSkfNa5UK3mWsataPMqSNO8EBctXPtpcWTK/Jpn9v9yr1zWrLx8kIILs+CRda6sn3NN5rXQ0LqFEGoWSCOHw4G+7/nuu+94//79rGHnqP1zOScLepSVhTCEQkUwRaaQStx8cfYVJkDV6Kc5MqRCCrng3hTHZWUn1JR5V0ikauRVyeITFeDON7imwfoG47w6TOdY45IoUl+Lo3WOt7dVXch412iZurZlmjpyjAzOz/2Wc1IHrdVEFN80+r2mwblmzs+oEMXsEJXLMf2AN7j26ty389jpQANSLEvRZKe2oW08zgrOZLyHtoW2Ae+ScpCI0rpmMrauK3UOzWHGesEls3qeU7NhWZWK4ujNi4KRygZOjYgCnFjVlo3DmQ0ZQ4oNOTuc3dA0W039t3YOQ9W1sAhw5zusa9XhX6gUqAW5c6U+rsrox7Uf1okZJ45nrR1ojODaBowwDJot6O17YnMmFf5i6yzb7U5N49LbyhQ2ghhuPnvNprvScksvXuCco/MqTL0VGivkeMPrF9dMY89/keDt3XsicHc8MIXAZr+n2+/YXV1x/eIF3umkctbiGuUocN5w01wTQ8fnL1/w9uUtV1cv+L2f/NO03Y7t51/Q7K9JttEYVpKaYgBxIsaJqY/07zQsMIYDPp4Axz43RGsJTYfYljEnToWhcCrhfj6N+EsVVp2Vzs11JivHSUqJn/70j/g7f+f/Sz8MnE4HYgx88cUXfPnlV1xf3/Dn//xfYLvdFYFoZk04F8y0CkNNuY+Mwzhzl4cQGIcdN7e3iAh3d+8QqTwf5tE6l4u5+SFIROxSk1IqXPZIaK+P77qOq6srdrutxnmX8LMQAt988w3/4O//fd7f3fG3/tbf4hdff81ut+P6+oZN1/GjL75gu9ks90QmRBhDph8Tp3PkdIqczolpTJxPkWHQEmWHB4XR7u/umMYBm6PySxuhaZsS0aTOYWs12shYy/bqiqZp2ey2tLu9VlEqglysR6yGDO6vb7XYQrfB+OJwNUUDnrOBS7/kXCh+1dzHaSJQ8poYY42w3+04HR9wRpjGkaMVxqEvRFITmkvRYZ3js9dfcH37BZvtlpA8YxDGACZqTrPJNYZHhfBz23Hl965DVjfkGvGUqcVpVaBvupbPXt0geeL22hDHzKsXmdsb2G8SmybS2IxNEyaFklttAeUhwVo94yyMSw5F0nmNZOXQABYnj6jvwWg1HGOVK2kYFB9P2YF49vtbdu4lTbPh6vo1YpwGR4yZ/f6Gly+/KJtsKT4tiUQq2nijkJdpENMgJiM2lXsIJV7ckpPHRft95PcPrYFrttgUgmqtJWohRA1ZC2HACDNu6aIjNQ6DU80wafrwcO7BGFJ6oRtBwRadc7SuRLaYUiWajN12xDCy3e0w1pIExqJFidGUed80NG2Dt5q44kpVFQq22zYNyRu2m5bdpuN6v+PV7Qs1ZzcbpPEEscp4uNK+c46kNJHCRDgesTEQwwM2nsB1+M5ryKJRUqOp0M+mlEoFGhUSj1t1zq2FXIyaQXh3d8fPfv5zzucTDw93M8Vo23YzgVLXFc2ghHBStdTCwxJCUK7ySmkbwqxgOe9om5aua2eKV7iEORZIW+Y/HocHPiec1e6+hE3WwlwzZN3sRK2FFOpGdjoe+e6773j79i0//eM/5o//+I958eIFr18PXO33vH75ElYCXCEcDaULQSvMh5AJAaZA+T0RJuV6GcfqCBxpCreHbqhW+bqLEqcRQapxO9/g2xbftFrU29jyajCuKSZ2i+82GqLplJBNBXzh96m84qV/KBueKfCiERVgSooZyXEzJwSdSzWksVduD+8drtH0/e1uh/cNm82eptlgXVucfhoVFpM6x1fBnx9e3xSOFS7nQfVPz8B0Odo5Q7dp6DpP2xjaBroOthvo2sJoKAlDQlLhESGtWP+MQiSF6yXnEtSA0d+r46VaxVnXZTbFk5a0P8dJGCat6BSTesrariWxRcyetnuBsV4Lt6SAb65ou5sSKKAafSISc9CxsL7g6Q6NVUxgJmoQqOYJFMvVfCi49fn2gwrwKQQeDg8M48Rms6XxugjbtsWICjDvLGHUlOBJ1AljzEL8E2Kmn6I6cozQdq2agcXsSzkhqTgIihA1JVtut91qVZoUefvZa0KY+PzFS25vbnDOczyeNJRos1GC9ujZZk3W2Gw7rGS+/OrHjP2Edx3JCGPUAsnW9GTTIE4nu0bBZGwKuBTwJnGzt2wBT4cDJjwnhEhmIHAOI2ONDBG06n122BghPoYlLmGLCiUYo1VKhlGLOdw93DOOA+/ev+Pm7Ruappmdb97ZQmMgVCKemuIepgk32sI2GIocVlilaRqur68xIiVb0wMLSdUsvIvV9JQ7mvm+a6vmtvJKXH42H18gg82sge9mB+Y4jpqkMgwcjkcOxyN93xeCtDSbuI/PW+P6K3lV/dFal3GO2JlZF3PSmH5rSSkwXrBngjivhTDals12h/OO3dUNTdfSdh3tdleEuyauiFFcWqwQ00QOquTM/VHi7bdbFbTKxQFK6wCCho1aY8gJYhnLOg7Wena7KybfEqeAtVpOz5zOyqgmliyWEBP9MIIx9IMWNmkmpQUQAZe038xsqj0dz+qX0bGtTg0V7MrJVROddNNx1rDbNkzXW7786hXdBrbdia49Y21gjImYwCWDyVlr+ogjZ0cW9QmkBLHEf08zL41oH4kyhuo4Vw3cAE7rcUZPFkPfZ059JmVLTB2II1rHCHRjJJoBawPDMOkmnu6ZssxQnrGl6pJTbN1ldVQijkotWyPRapiDEt+pMpCeYwb7QPthIZQQuL9/4DwMhJBp28h2I7TtFhGPd1qr75QPDOcTKWcOhwegVLHJWbkOstWCviJ0mw2+aUpIYXXkRa2RWMymCsnudjs+//w1TuDw3XeEaeLLV6+4vr7mNI7cH4+INXRG8NnTxZYrMo217HZ7Ouf48Y9/j9a39H3g7r5nDBEZBsXcXCoLQpiyZre1paaes/Bia7myFuuV9Og4Caez6KbUTxzDog1WjFCMQYZRT1aarP99pIlr2rU6F0/nI3f37zmfz+z3e609ud0UKs+Md45Nq0RZMldJ0vNO04gbLNM0MY4DkEnJljTjlpvrG1wJuXTOF2w4zXg5VCflbxbe2vT+a0Hk5wR4pe/dbDbcXF9ztd/TFHKoSmfb9z0PDw88PDxwPp20YEaKs+Xx2F5VayMWwa18IOMUFsE91cSX4k9IUcMSnSWO+v5CvKWl7nzT0m02XN2+UIfr1RVN0cCbrkShrCJ2Mpo2rs60wDCMTGOY8XTnnGraZVarjNQw1IxFSqHkJJBCYC71lcHaht3uhthOpBiVo2UMiH2YIRrEMpU5iBjO/UjMmW5s8Y3XbGVX6zaWQiXPjGvlqOfiyRYCs7nnLUrt6wz7fQfs+NFPXnN97UnpDTkmJBvGYvX5bHBkFdhFcMfJk8RjIkw2IpKUtlUyrlDyakifQhQ5CVq8skHoSBjG4AnJcu4zx3Mtyqz0sEN2nEKmaxNT6rG2+Ohi4NCP3J+PWGs0VNDbEmbaKi0wXbFsHWQ7++6YIagauaYW3ZpW+Te1H1SAs9LMclIWtTBNDP1AionTyRX6xnGOUa4TJZbCwWLUsei8KzwdNSNQvdxKmkDREpbU7ZwiXdtwfXVFGgc+e/WSME1sN52yoqXCs1yZ79BzZM3uwGb1+ndNy3azJ+eBbLWQwpQSKQQ1l5IS/LclcaPJiTZnfPGCB5TtjjRyHOF4EvpoGEZKKTU1tBQ31PhtG+MF2eS8NFaLaI53LhwhtnBMAyQ0MUpTypXwv9ZHVAG7OJhmjLNCKnlhFVwWomKy3nt1zJVwxuVnNc4VPqn/ftBa/JBX7BFuzhpCURjusXZ96YDV+62Fhp8JnqBGTFz0bi6b0aruqjFCWlsNxdFX63gqmVVL03Y0bVccg16dg4XVUivhSNk0Wea4SElUgjAGxmGcIxWc8+x3gbbJ4LRIspVars3MFehZjVVN2NGYas0CbjoNfdtse7a7fRHwisXnQqEbglpdNprCcBmJtmiMHxq6uUOKJSJQ+WarUx9qjFIlOdNUf2M12/pqf6Xw6RiYxghpJE2CZK2oYwnE5IipBDIYrbKp3EWq8YsoJQbGkSk+hpIrIqJMgsa12Gaj2jaOFA2IzpeYhSkqvj9MERlGYkaj2oxRLpsccdbQjxpeOU4aEtl1gXGKOOfYbk0pxbje0Mpczktf1dn2mzt2aT94HLhFsIjWqmTkOAXOxzPWGk73Sj7ESjjVRRkKiVTTOc0O2+7Y77fsNhudhFnNFOVUz6XgrE6eFJUD+tXtDS+utxw+/4zPb/YMQ8/9wwP9MIAzZKvxmdmawvmQyCEgIeKj0CXL7faGRjq+uz/yy2PS0LMpkoYTXRfYOS2n9WLT4L1RCCVGGsmMaDjf/fme4/DA+Zx5/zYyRcN7e83JtLqBlKzCev87B1t/Wfq00sZqfUkV3jXlv910bHdbpjDOQnycRg7HA8fTkdPxxGl3oms6YtMqT3MdoOJoUn7v6eKnFm4QI2y3O4WWug2Nb+can6nwgcwJODMWvt4euMDuP6CkczmzlzT3ru24ubllf3VF23U0TTMz/63x8DqHagGFplpqj2allIQOM+8vymKZU1DXm9XQS984hEQvBesVAeMw1ql/wTn2V1d0mw1N27Ld77HO0W63c5SJb9tVyJmSU6UQSw1UFZjHe7UiwqQl97xv2LXXtHbHpvF0zV75W5xGs6jl2jDlcbZElAb3Dfv9jtefvaRpWtq2BTLddkfbbZmmiePxOK+t8/lEJtGfO1KKbHqdw4Ij+FJWrTjnn2rgxfE9jWXu1uSuMgYlkkxhsopjZ5w3bPdb/sw/9ee0mtT9txwPbwjTmfPpDSmONHLGysj5HBjue1IWxDuwDc5u8J1DJJMl6pyR4lw0gnGmbL5awKTdbNle35KS4f2d0PdwHidCmggxcSo1Qs/hhDudMUb47q2ZI0iqs71WWtq0G6zzbLuW7WZD17W8fv2Crmu4ub7i6qoWY6mad4ELM1TX7vdpP6wGTsFuqRq4VrDJadIwOiLe2znsqoZhCRVa0N3dFmZAa2ux3WV3y7LKqqsOg6QD2zYe51qcgTi+ZOh7Qgqq3SO0WRMcYulaA3OPS9JKOV4sjW/xbkSMBROJxUmYk8HmgCPRGUNjLSZHjERs1mLFU4och5H7U09/ypyOkRANQ9sSrIA1SC6B/kmzSFtx4B8Tvj81Y6sW6mqYnXOstdI5qSVWSKCQh6VEqg6yUlVkrpn56GfWaOfSUJehfHNmJYuGUedv5UHJBS5Z339dGBUzfYYNheootbaGMfo5fPNDGPc6TdpY88x5Vw7Too3L6ooVjxdRov9UoDqkflgyX70v4agKoTivWrexVqNEqiPSVE4MHZtaB6AmuOUEsThNx3Fi6EdSVA5vLb4gJaO0OOmkcrzILFhT0sStYRjpuq5czxYGUNhsRja7HjsM9P2gPPLFcR0L1l+Lm+irUes3S6G0eV7oLL6APJfUm5kLxCCmWje1A7VfnVOIMsVMjppSP9mGEEZSHHBisOKQsVcOkiwFTrOIbXC+0/Oi68WILQIW9VcIGDNhTMQVeCslgz+po1qdtFkjxGIkpFyiStRJHcNK0NZJkRV2jEGwNpb6l4YY4PpqwohRojgqcZs+d7VEL8JqP96H+UNHoWRSHElhIKREMnbWJEUgTKakt+ru5qxls93Mi69pLU1j8C5jTSKOZ4bjvS6cpi1VaIqppjRqakIWyLFrGrZdQ+cF53SSt7uW0+moZmuvOOH52BNCpPWWLkTs+czh22+YjOU0Bvox0A8jmzTgJeDaHtNMbLrAdafcFC4cMREODyceHo5MU+DwMJSFeWKaBlz0NNNGSaj2CdNpseBx1Io5YdIaoe31Htr9E/xWpGKvOhls8dy0Xctut2UYejTDUMP/aoHb0/nM8XyibTutUl5iw1Vz1nNM01g0b9XOUtQMxBAiuRIvGTdvtupo1govlRKlmvQXevTsoFyIrirskiQrDS9CMuW1wAgiQuMbRDSdf7ffs9lsCme7oWk8KbXs9zs+++wVxgivXr1inEZevnzJyxcv6DqNVOLR/Vir6d9d1xKTcmZsNuq4iuV5rSukWa4UGiArvJkUhmjbtnCeyGy95NMJMYZ+HBFrVQveaJm1ru20iENQRQbQ0EAx7HdbnFEq02lU+lnvHGEKnI8n3hdCKWO0Dzfblq5rCsyh6d9X13tEEtvthqZryuZV5ou3NF0LRmjO6gOpnDahkrQJ87wJzmgBiyLGsjyngTPDlWrBpIpGoWBgIlKhKArs5DDGI9jC6w/XVy/oXEOMA7c3O1IamYZvieGBkO+QhwdMht3Vjqa94ubmBS9fvAZRH4LCpZXLJyNo9AdyQKTn6nrLq89adYTi6U6WYXrAvZ9IOWNFrbzbqz031zuMhcYXuCtVSM7M68QaJeSrpfGUM73F26Zs0kspPd1gKzxpiAmiNd9Hfn9URZ7fA/594Et9cv56zvl/LSIvgf8A+H3gj4B/Pef87ntcWzWMOJHCqDtcKSs1V3IpO6jeiGbciblVQqHtBt94nDc4C0ZUgI+nA857dVmKkE011yAbNfuc1/jZ1jv2XUvsHO3eE1Ngs2s1fnqYSKeRME48vHnP2A9zIoyJgWN/ps9wGif6MTKR6YhgEjs30JqRTRu5avT+a1mxd3f3vP3mjsNp5I9/eaAfAi5nXIYru+XLpsU4MD4hLhHTSB+1VNnUj+QUuWodsF935PwjUl+ZBWnbNmw3W47dkVww91DrVoaoNJ7nM1131r5baYSmaIohFOEdQonLTwWaKBSzq+wzWyh/dTOumOQSCbEW4nNJLXmKs1+EC5ZIhcpVrRCRarC+adlud0r+VDZ37x05N+x2W16+fAHA7YtbTucTNzc33N7clIiVS0umatbWar+lbAkRuk2HMRP9uSfUKANrsKkUjEDxaLJaBE1bQxoLBp/V14AIuYSjNm1LF6bCA67Ydi4Vm0QEZyxWhO1mS9u0uqnFGrFhiUF57lOIJbdHx3+cWqapwRozl0Hb77d4rxubb32BllQxMM7iupZsNOs3U4RfyeoNk1rEdd5UaEyLGHwYsp1pU3OhUFjhvJU1EBYBbq3gpNHiDa7BoOG0dDsyEyldkdLA/UPmfBZO44g41Wh3uw3b3TWfvfqSr774fRAYgwrhaaoO9UAIZ3IOmvGahd1Vx4uXLTl7QtjSNJ67hwnrDDZp1a5E5sXumi9fv8Y5oe1Um9cygRlNl9fMzKUknQr4pvF416r1VemyS/GYWdWuMEoyhPjUavx17WM08AD8z3PO/28RuQL+XyLyfwX+J8B/mnP+ayLyV4C/Avw7H31l6sKtDvhKFq+TUMiktXkmIJKYpoGcI2JRjEsyQ9+Sc+J0uKcxnqZUj9FFrvUTKQtOshCNerWH84ljmogpMIZeXw9HLec0jIRzT5oiMvXYFJCcMUYDgaTcm8PSWsHmTC6E/o14LImchKGfyCkz9IqpHR9GTseJYUiIqLboAZ/B+xbfaWyw326J28I1nLxCOa2eq+22H9O7s2Ds2o7b21tCDLy4fcE0Ba7213TdFu99wVZHTudeqw2ZWv2kwhCmCOupFBCIJQtx4fKui7hpGna7HTlnNpuDFkue+cAfcaHAPKH1zTy/qOlb+FREYQKFctRvYK3j6uqKtu3ouk1xSMq8qaQ5CqTl5uYGEeHHP/oxjW/48suv+OzVa4wRHev0uCBvnp2Uqu0qayTVgVU4TowI2Ric8wUWUmlmjJ152OdUepXts0MvZ8gxEKcBUmRylhwDOZbkF4SpONCrAz9FLUAMMI5uFuTOaamxUpKVYWjp+1ajirZaVk5DRSM5G1LS7NPquA4xFIEdi/WhmqOQaVZEZUobbB4911PH8m9sZSdXZ61qoSQKmVkkERmnUbmK8oSRgBGNUsspcf+gOQkiht32BjGO3e6a7faqWFUKOdqsZe6iKf6JkvquIZel2lJJYtLEN+F8mhiHgRjV6ri+3mCN47NX13z+2Y36P4oGHgstrzqPHWTN3q1rI6ZcLLkK75n58WGBT7QPKgb+/drHlFT7Gvi6/P4gIn8X+DHwl4F/pRz27wF/k99CgHubmaziTdX5Xv1KWZly9HcAAqfzPQB+9PizVkrJaaTxLS4kzm/f0W23XN3c4Jxjs91qNlvjyU2jjpdoiCKEh/fch0AcR8LxQIqBNI2aZDMNjOcjkHHG4kWUId43KIuhag27QnITU2YMGqUSB0jREYeB9+/PhCnycN8zjJE3dwe+uzsRMTi31eQFDI0Y9u2W/c0LTNMQP3uF3e/oBHalD0wWDBkXT0g8zr1Sd/BKIZJzFd662G5vbvnzf+7P8/LlK+7u7ri9ecFmu2O33bHbXXE694h5YAqJw/FcChr4GT+vlKsaIbFU64lBC0AIujZSTlxfX/PVVz/i/uGOjGpvKS/hhHnejyvGLDPZ0ON0+SpE1Vm5FHLOGbxv+Oyz12w2W16+fKWx52LmaA3tGsN+f8Wf/bN/jmEYePXqc46HI9fX17x88YpxGPjjn/7hHJq6zDT98V61+ZxguIoMzcjpeNQMVK+atxFBthtSbObiBtWBrDi6mWvbai5Jce7mTBwjfRg0xC0OhbtlJIaRtU/DFG7vMAXFwFOaSbXm/hTUsjSG3X7Ldruh7Vpub9XS2O22dF1LiJFx0k1gKEXDh2GkH89KjVu4vjddCznhvGV7tSnvdYUPX/nxTRHiHxQ6mVLAY/1eJUXLam1IjbICSZGce6Y4MQ5vyWmg84HWBzZty83tDTlnvv7VkfuHe0Qavvzyz+Jcx8sXP2GzuWazuaJtWypZVkoQktHyZ6KkV5rVnAlxYhhg6CEE4d2bA+/fCXfvDwz9gW6z4fd/70dc7ff8/u/9iN/70RczNp4zs69AKKRgRdtPMRFKgRQRwTV+jghbCNvW+Q2CKeRaVan92Pa9MHAR+X3gXwT+n8AXRbiTc/5aRD7/wHf+APgDgJubm+c+V4Gdlnhn6osBzALw15TunMEEQDR9eBoHJMNwPuGyRk34xuGdxxohpYCkiMnKUiahZKwNPQwjaRyZHh60Jl2cIAXCNDCNZxUipbaiYKk8+aawsVlnsMYiUYW3Mr9qem9MhjRl4pQIYyIMkWnSMlDZGIy3moUnRh0zzmG8wzQe2zTYppk1VEGjdQRBxgDx+KQvVXhXQSizwNRQpi3jOHJ9fcMwjrRNp4kkTUd1UoZS+1F9EYXPoxRgrjpWDWXTiMNFyKRyXdXeWrpJNePg7Ko03horXfG2zF7CtQA382ZeBbiImbWXpmnYFJpf7/zsMKs0wFI0ZGMsm81Gs1pvA13b6ca13c0FLz7UasahctRrFXNfyu9VZyk2k0o4YkYdj7NgKw7YJdSGAifUWPGSkJUMwZrCizMQwsg6LNQWFrwwBaZJ614O/TDHnKfCuKchtIIQIGsdzLZ1xNjQNBbv1EIMBTWKUR32FS4pamDRsDOSzVy44cLyMMsG+9GyZplAJW58eV9y7SxVEFKaGMcjMZ4xBIxEXAxkuuLMVyy+8Z1Ws/EdbbuhadSnoQqB8gmpMlNuVGQpJJQ0o1adsqE4hmEa0Sg1MtYI223Lfr9ht+vYblsylUu/VsnS+ai5F2CNavNTTNhYhHMtsF6s0Opcrr1XBXZx435sjwLfQ4CLyB74PwH/s5zz/cfiNDnnvw78dYAf/ehHF7ZWFYCKQ9bOLg8kGbFR+Sr16KJ5FNauFAhjYMiJozGM1sMQOfl72qbl/p2S4e+3ezX/SqYXKcHUk1Mg92fy0BPHiel4VnPIaE0+5y1tp6RWL168oO06Oi9srMadb3YbjDXEEg0Qp8R40Gr3MgkuNsiYMLElZof1hkki0wSnIZKMJTUejMVVMqPW03ctpumIvgXXMRMVUeVbBp46OkJMjGMojmFVe9SBqAWXr69u8L7lL/5z/zyH44EiFmmahpubFwuftvOIKZuSyEy6MwvrnDWyR1Ip95Y0aqXytHjP7e0NbdvgnJ3rPlamtVoYYo1zL5waZaTNkrhTiY7M7PwpkTXOc319TdMoU+P793d0fY+gpc8qEVlKGe87rG14ZX0pDM2Fxv+4aUSxRjiJyXRN5mrv6Bph7Pd0jePeW3IsjuWoYa05KrdNilr4ForVAixRUEUApIWFTkQYnIa5pZK8IzrlEWq9T8X/p0lxXYkJm5PSpRapFIPSfvSpJ40HxrbBpB7vHeNpS9tpzdim0CZYr68x1IiXcm8xz9mLTpSv3jeOtrF0ZSNwpvKrV9jzucVPQUY1dHcmQENhU2td6eM6xwJhnOj7I1//4k84n+9p3ETjAptNy9s332HE8Pb9A1PItG3HfvcF3nc0zRXWdZo0F7VEWhY0qSlmfEYtjX5kCiPHY88w9FoXNlhShBxHSInWG25v9lzt97x6dcvNzRW+Ffpw1KigmCGvsp2LpZvnTUMfvtYtJYUymFVgC7U49UJ69du1jxLgIuJR4f2/zzn/n8vbvxKRr4r2/RXwzfe9uIaAqSdfFFkuDi8QyRiXmenDkBLVABFliItRecIHsQQzwhQZrRJPHZ2SWPWbPY1vMFkjGlIYGY/3KnBOR9JwJk6B8aSmt/EgTtjut1y/vNa44u2G7Cw+ew09cpa2a7DO0edROUpi1urkIdNEg80GExwueVJW/uRoEg9NoG1Gohii81p3sXqknWP0HuM9yTVg1KFoqidgDtt7OuBV+7wQ4CljjNJiKrdFx5/5M0r0VIvhimhM9BJ2p9prXVRznHQV3mVS1kCqVBKw4jhRC/RqmrfHeUutpF5DDutrDVmckcDV5K67U/XUS9Gklwlf+Vu2cxTJ8XAkTAHnGppmKhsGM/FT9QUADMPA+Xh6FrpVLUifzlYCMgfbzjE54fpqoxVaYuJ0HBmNpT9PpRJdnIsmpML6uDhlFxgphmkmeqpFjseS6q2RI6Uwg6lhkpqMpZWpFkxCiZtUo005zU61MVriaIhjgzMB5x1hPOJbj/WettMY9c3uBu870mpDU76dvPSEJJxXC6TxFu+0nJkxFOLbBXJ60qpJWPwWZN0YqBalMas5JoSUiWliGHrevHnD4eEtzkxYO7HpPOfzA9ZazidLjAYjDZvuBd53yvRnHZC08pCglARFA7ZZYJwYSjjm8TTSn0duroQU2+JkHcgp4N2W/XbDfrfl+nrH1fUO64Ux9JAhTXq/CpPZFRSygj9WCk+qu/G6a+axdWXeLVbN92kfE4UiwP8W+Ls55//V6qP/GPi3gL9WXv+j73dptDMKPadZVy036rDMsfL3FvMjKVF+dWYp7WcuKc2JPkIwE7odaEZa74+aqUbGZiAF8nSGFLFx1HqDOYIrJqRSNZMYiVNPkMTp/j1pGEinA+Fe6WmPu71iWtEoj8KUMX1EErXYlRLaOP18xDKlTNwIVprauSoEa8KLtUTrybbGCcvSUfPr84slBp2YoGFT1VxLadkEU8o468BDsqlMwCXipGq3FOx2Pemq8K3x48r93TOcT4Qw0Z+OpBBmDpIQJk1bXwmxKuwVB15S7B8/TnWOVeENCy6+hl5041GIpNts2Gw2pIwWmy7VZvT5CoZfYILD4cD79+8Zh55hGD48OSkaZoFybIluadtMt2nZ7jTqpT8P+oxhIhRhGsqc1DjqUo8xKV+4prcXLo6igTtXa7Pq+C5+ALmoJHSx0ZVnSiRlByz9bEQwGXKMTP1AnCZSCNjBYqzj3J6x1jGOiabRYgMYdcJVGg7ntP7lplOSsqbRXAvnlLHPSN1683PTUedkVLa+OYuilvpb3f/yvBRyusw0ZPojnA5oRE5OnLtIij3eOZzbY90GsV5jtXMsr0VIFLhuimqtHI49p/PA6Tzw3Zv3TOPA+XRiHEeOJ+F87jUqrTFcXXli3hLyLdvtTtdLZg6X1RCdSkGbqbVgjYmQcylXV+mWl0CCtWRerKvye861ROwHrcIPtY/RwP97wP8Y+Nsi8l+U9/4XqOD+D0Xk3wb+GPjXvteVUfNSmd3SzKI2yw5SKd6gtTBt0QSddViT59R7klZYlwx9PCuWPkXiUMj1tQ4VTvRhnYHOCc7AtrFsvFUB54sZaBMY1ZjGcyQNlvd9jxOPz5km6X1a1yLGsttc0XU7Wue5brfqAPTq5IzGkRtPyMLJGM7ZMG1uNUIhRWQ8kWJkOJ2Yhp7k1ClbTdtZ86ZqWXWFXQ5yztoHp+PpYqHPC361q3unHDNSqEdhWbTz1qBdBihlgZr6yoaoTq+BaZo4PNxzuL9nHHru3r1lmsaZMGpdWKIKXg3DXLIMK1VtfYbLyVuF1iKo1pvIcgxsd3s22x37/Z7T+cxms53JrdTh7NTBNI7EGPj22+/4xZ/8HCPC1dW2ZPvOvbm6g+pEBGv1frbbBuccWldR6PuBoR8RhDiN5dk0IzfFyDD0TIXFMYzVwRpnbdRandvStbrRlFWtLqBinpf5f7mJlfGJiWgUnnCmZruW0L0pcHpQB22uRpsmVmCs4+r6vqT4d7TdVrN2O+XD77qG65srNl3D9fWucHVbdewaMBXaTMw+qss5CdMUOKXCMtj6ErmT501xKcesMeUpJoY+cT5l7t7Bu7fC2CemIdA0kbfXgbZ1/OTPvODlq1us6wg5QBowaSG4Iysh2cPhzDRNfPvmHW/v7jmfB95896D1BuIR8sBuM3F/HzWkeLdlv20w9iXG/QTrG1rfQjLKxJl0nNUmXtbXRfHyOoWkZqpKYZBcNuQlwWm16nImr6icP7Z9TBTK/4MLEXDR/tWPv9SvuwizoFq/lCRA9d4vSMqimRTHQX3mXEz8WLREUiYHFfKxeLujUQ05Wy2KmkqqeHXTmWKSSkwggSxZi5OKlpvOsWDQthQ9jgaJgriGMam5GxPKJmc9wcOEYcQwIgRjSFYLLyVjNTyscGokgTA/UMZU21aWjvrQ2A7DwOFwmL3dF/jarAAsGoEpAjyXfl7r9XkW4CXMKmflqZkFeF8iInr685lh6DmdjozjyFiq36wn6RzFssrenBfb7KBeD/wiqDTRIZeoizhvAjUETRBiWjT6h4fDHJuurImL938sJdXu7u54OBxw1rDdto8E+GWrxKlVidL+Lan03hFCZKn1eGlpzBmMj2AkipAVMZhUfAo5kUu4ig5V0W9Xm9paCNSuEi5f55lSIa9c0PeS4UlSB7rNucR3a+IVKSmcZ2R+tqbx+EIOVuPea0io9nyeieGeayFEcgjEwitkcwZbAcA8R+dUTV6nQ9VLNf09RqFk4zOO2gc5lYQfUeVLC5VHSDLnKExT4HQ6M04Tx9OZ4+lEfx611kCISkmbNaBgGiNWhNZr+KyxvhTRcDirsG5KT0XgMjYa7bLSqalRJoBaRs+NTXluhR2+J3ZS2g+cSi/krFy9GVng7lTTTUvkgSn9ISXWVSgxqepVjqFOVKsHZCF7jTmOiApyMkEy2QiTNSQj9IXswgGblLGAnxIu60+Tg9b8S2fIwpCEU0KrojgHRjjfn2mb95o0Ud4bGiFYwXRbZHdLdp6xuyW6llOMHKsTa1Q2u5SEJFax69MB60ZaEVyMpeahOns+BKDEGPnHf/iHnM9/D+u0PqQpsdzGlIQTq0kls1ZQnKNrYVm1iaxDMzvZMrk45rRM3Pl8JoTAw/0d93d3jMPA+/dvi4ZbBexq0501Rim4rpnHr7ICGqPO7IoLWmvm709T4O3bt9zf3zNNY4E98mxddN2GbrOl6zY8PNzTNA1t2+Ebr5BF2djP517DGouA3W433Nzs6br2Yk4+P1V1w3BOI2K6rmG317wA31jsKKQc6Qe9xvl8Lpj0kuhU7yenWKAT5nENxTJRISmKjcfqJ1qSjSr0EMuGkKISk1EUD4oPZI5tL9cwTiOeVLUvhR8UdMdbw3aj+RO3L29oWw0/vL69xjvDZuPn0NmqDEipj5BrBMljjDdn3t7dc7x/T9t6Xtxe4b1ju21pm1LJvmqmqfaFwTWedtNx8/IWrBCjhrZKssS4JaUWMVc4d60wijNgMv3QkzIcDwP392eGYeTN27cMw8j96cDxdCLGzDgUpNSAFce5h3fvBva7zKt/asvNzQ05bUnZIcbiGsFYCNGRUskuDkXJqTQBpTJTzlU41zWqfVOdlM9BYBYBu6y9qnB9bPvh2QizZkTpQ6+1jbpAlww9Y/IMzRqjLGw1mD7lsgGIYOxS8UKsIUuqDnFEIBZHyiSVfl0no8kZFzIuZVxM2KAeflHuK0IWhqwkPFnTP0ljYHTneUCywKEVBmdw2yt8EsR3iN2COE45c0qK0xEjUlOMRc2MfhqxKSPTqEki+FLu6sNh/iln3r55w6++/a6EDG6KQPSzYPRecW5buGSERXgvhSpqma7LQcrkRVjEOBcHrgJ8GAbu3r/VdPGKu1e9Ki8nq8LaliLErlShsVZmYb6QcOnUrOnKfa8WxjD0nM8nnSMlw/Pc97TnM23bzZEwystsZignpcTpdCJME23bsdtsCyz0OIlnnoD1l0XbFTBWgRXnLU3jSxk+jdjIWakPNGt1nCkhKuarsezFoppxq1ycwTpDRYqCAFRissdaeNXyF+1eIRNTz5fT7Bylau2FSpXqXyhJQpqcJkqB2jh2246u69jtN+x2m5K5XDNmyyqqqFyV2/n5eXk+97x9d1fKGtpC9VsiNrAa0su6zJnMzKLdtmOKE/atJ2aI2ZByQ84tIi3GdqqFF4qMKWq25eF44t27B879wLffvqUfBs7DmX4a0ES7psAgGrsdQuJ8CviSvHR1tSGGhhA0Xtg2VWG0Si8b0GT8OZpK5Uydp8v7Sz+k4ltKa7/Gii+InFd+gaeb4a9rPywXCotZPdeLLJ9IXscJa+WK6s0XowvJGCkVdLSKTMx5TmUNxaTOqLpgDLiSwpotJCNEY5iMCk6HwUomGIsjY3OmKZltRnSSTWKYROsbZmdUm5dCFg8gQgIesqGPghkC5v6A8SNb1+DCwGQd2SrfiCs1E1MNOYqRNA5gAqO1pDASnCe2rYb2eX01jzIHc848HO755ptfzgJQ03UrUZL+aNyzGr9VjCNSsPCyqEvIYvGuLBZh0TJSUi6VmCLn00mFYpg4n04F814EX9XwayigdlHRIGNNozbUePVKh1rrddaIE+89V1dXxSnJReHk6sQea0alXG4UtehEKtWbUkp0bcf+as9ut8M+SqWv96jIwMrGr79KgZGtauPeKz9PSonttqPrWoZy4KyNFQe9dueyYKs9NUcgsFrc9T5WUNg6dHddOq/2pSv3nXKJY1lZ51a01Jr1lqZEUF3dXNN2LVc3V7x4eUvTtOyvtjRNQ9O4Yu0uIYK1ILA+068XMjmXcod9IMSMmPd4ZxnGgc2mpWt82SBMyVoVhmniPA6cjicejj2H00DG0m32bLdbPv/8Nd2mY7PdIWIZx8i7dydSShwOA8MYeHg4c/fuxDhNnM4TU4haTKXIgQWiqT8lblyaUvt0YJoeGActErPr9jjvydmSsUxGCMFoGU6ryqc6MnU+VgFeBfraUV/lF8i8OeuxFkkOm5lr1n5s+8HJrKrJjSTmqh0lAkC5A4pmniKqoSixfdM6jNFMOOc1dXaoGCmZsQhwKypAjFcHDDWGmaxQighRFMYxqJC36GJoiqZhvYZMRWs0qkSE5JRfJQV1VgEgQkS4S4ZzEnIYiacR7x2fm8xm00LTgG8Q29A21xjjmHJmyhnCRAyJKEapAKzDeI9rNH63213hfEOOjwR4Srx795Y//tkfLRir3hCwhKOJlMKpM4ZZV6YpR69NvOITWDle5miS6igqTIbze2RsyUxVuMTNEIBZJeFYm+eQx1p8oWr/xihzYlO4s7uuI0yB29sX1FC8+/u7GTZAIBSiLQQeDvflGjWDdOmL6gh8+eKWFy9esN1u8KXA77rNG5ysvr3qKkGdmprc47i+3uKd5fhw4OH9PZIz92V+19hvhTj0HPOGv2pGZOYRr32u++vC6/543VRK31reLhdYat7cKAJFwItFnKfpOq5urmhaz2eff8Z2v+Xqes/Ny5uS9q3heKotL5r343DB2Wcx+y6eAuHjGDmcRkQSD8cHjBGuH7ZsNg277ZbPXt7qNRst7Xc4Hbk7PnA6nXn3/sDpdCbh2OxfcHt7zU9+//fZbpSoC7Gc+5HDmwPjNPH27T2n80B/njgehzkSKFE43KvpLqXEmmhKPeIQaQHHNCX64czQ95zP72i7husXnyntrnRAhzWGoffktFAA5wzeL1w/QJFrNWw0zjBkhbZyQQtiKQhirMOlxDjFmW7iY9oPTie7JHRcauDakpo9KxJ9FTCr4+pEr3E45ZyVGN8W7c9aNc0QdcxBUijEGK1/KFqoFVPqnGTdtQ1giy6WrZBsEeAFP4/FEVnARSIQkgrknDMJ1WCmMOKDzPihAXKadGBzKPwUFb5ISDDMMcGSyckrVzdgeWr2V01sjuyY72nVb1JJ9Fk0cHhGgF/2t6zGaT1mGpccFy1DgJkSwZBznB1u2SwFAFLS68QVcU8saccKnaiA8j7Mny2cE4u2OgtHyiafL8XIHClBFYSq2Xvf4EsBiA8mpJXnVjl+CV7JPNXyvEHZklrflBBGYy1SNORcOA5mxKFMW93z8rxRCIvfod7zur/X71XBXpkZL6KNFpV53gS0FqfWelV+8oZuu6HbdLQlTNBaq2yOZon+mROQ6vOSqSTAcNnfj9t6s885YQwMo260xlgOxzPOWcKUNHfjfOZ8GjTZZio0DcaWRKK2wGKOqVRM6vuBw/HMNAZO55G+nxjGMFvfNYmqCu3a0aqoaVZt4xt8o/wpIUT6fmAcEuOQENMQpg3BRd1c56o6roz9YiXBpQCvVmXOmWzzXFuW1fiyGjJyTVBc9/dvbj8wBq7pxDlHhSnMwpULFGGkjjdjZV6EasbO0nrGEetrzJmxZMJ5azFWY4Vvb/bEHDn1J02IsJCNFmtNiG4CxQlqqJW3lapWUC3JlkWRimMiG60mrepOJuZMP/b001QlIJnE+XRULuPG44IjuQabItZY+lH5KbTeoXJ62DSWiaEbhrGeGEZ800KTabpHQiVrvOyFllRjSdJqS5yF8HogigCfMbh8cYAKm0tzUF9WQn0+NgFhxl1hmeAK66g32tbXCncYdfIZY2kKhu29p9tsAGEchyLkKTU3hSmMc2KRFO2qas6m/DcLMOt48eIl2+2WV69fc3P7grZRKOFpq8J7sVYwSvVgEnOh6oSSqTWt1mC9vrlhHCLet9zdPYAYzqcjwzBp5uGMO+tOWkP+pISPZYRYIMVajkxEZt9CpeoFKRaKKxq4n7X9TF7BLYJWQzdsr6/Z7nZc31zzxVdf0HYNLz67odu2tK2j2zY6v13hzpZMRnnzc4GBFsNBSxnWqZYfbZy1JSDWhKrSjeGhxxx77u6OvHnzvhSgUAglhKTFxUPgeBgJMXNztWO/v2a37cB4xghv3rzh/uGBYZg4HvrCUJmW3BB0q0nV55BNWRtogITJmqiz3fDZyx1fvH6Bc5H793/C3btDoTPo6TYt3h/ZbDY07Wc0zQtCaInRUTnMq1JhjL3oC2Ms1kS9l1XyWiz+ihqhVbZlhYCLBf59YsF/YA28CpsabqbvGbMyPwGonVRIpGQJ61pOVXfb9c6vb4iIOkYKmc8QRuU9KVaUHqp4Vhaj+cjVkC6bhSFrHHk1w0Tm3V2xNYASwljMt4oppySEMGGNSgCxEcmpZGJqUdkQMyY7JbTJAlkZ6XIuEV5uYmoayIlkrWoBj5THi+6oVsPK1K1agXZXvjh60dYXx8p6WX4o6WYNMyzH1TtJC0yzOv/j5JyqTTqvuLd3GrrmvacbhlnwX2qfMi+YKmzrY8yYcjm+xlF3bcd2u9OqQW1bmCo/lMYs879V0zJVja49nDOIRo64on13m45xGHHeY8dSjWbeZBT3TPOUnwdj3gyrlmbmOc8K5y6RIFBwV3uhpYdSqGQukowUpcDOpd26zYbdfk+7adjutnTbFucF5/V6dZPRcy5EZZclB6r5MN8+z8mcnBeOHO2IEv9PZpDM+Vxpc9W5PvOUxMQ0qfJmrKfrtM5tFkNMmcOp5937e4YhcDoO5dqqEBhjkMr6txrDnK1aUjlp2odzbLqWzWbDZrPHyMjd+8AwHMnpTIoncm45nzaQA+QtwpaYimV8oeAszn+pc9IsnCe5rikRZGYwXNEnz31YMPI/LQJctRiFCYw1JUTLzJM0FoekMaJUsKLxt7PwnqEBXbyz5Yj2gRGjdKNNy26rRXynaaKf+qJhyMIUl+pCWPTXei6ZNS4hlI/WNUmlHi7F2E5ZI0wQjC8JM2LJGGKEPAaCzWQOGLGEpFZATILEoWwdlf9EzT8T1Gsfx4aN28F2z1pk51wcuI9xBKA+ZNVOa1/lIvEWLHw5ZpmgzwjxZ8dy6fsnO0vp10UAP/6WCos0Q0AJE6zW7AzTLOBFNHGmajzOqXNp0YQo0RVSCjsont61Wmbt1avP2F9dsdluVXM1Mn9/afLkN8mUEL1FoJedXYsFiCUBpunodlcMU6Lb7ogJzucBkaGgGcumMz95mcgxVSGuxF9GRKvezFacCiQbq3ZuFw72Eudusmp8FCGmDuAWax03L2+5ur7h+uaK/Y0Wf25aj3OmKEUKY1ZBQ9Z5sd5n5NFPHcJFBXt+XuR5l69glFKthhQ1yqVrMM7SOoV4pikQ0x3TFOj7M2/fv8U7x8NRk5LevHnPw+Gsgj4utAMG0ao5ZWY/2nLKJu5pvOP1Z7d8/uol1zcNxnlSjNwfAg8PAzAieaQdBNuc6LrM1fXA/iogkuZYeIXH1tCJzHrPejOenfRVsSjRVqqdq7Jpslp2xuT1AvmN7QfHwFMuqcDGlskkOKcPECtBWnmeys9c/16JWA0RXHMUZ90Z23bDdrNlv7/i6uqacew59A8oN7LMm51O1HyhMeQqmcvfkdVEnRVNmaelUDTzlNTRaEzRLjQkKWMJMWptRZkIQWESKZXA6yQAQVKd9KVUlDGQR5xzXG2Ey4IOZSLEZRmtN7OVKJo1qvlKq8nyQTy4XuPXCvAi4FbnWB//4e/m+fMYtR9D4GISPw67Wmvj6j9ZhUSWaBtXtPhNt+HmRmObX3/2mv3VFa5pCtUnpPzUiflUiKtQUFaZGo6q8EQWIUkiCrhmQ7d3jAG63RUxCf7hiIibzyMozplmBUSvE5NCMrqJFaKj4tzVuaOzzKWIQR3ymhHqlNSKjCmvWAuF13vT7XDec/vqFbcvbtlfbbm+uVKytlY5esSkAn2VkawaLWpZzrFFdXxzLmULF83xOQG+aJbak3NUGFr4IEwBa4XNpsM6z2a34+rqimEYOJ1OxBg49ydO/alAFoaUNaR0HKfZOW+MwRlT1n7BvldTuTqPnbXsNh2bruHL16/48Y8+x3mwLjOFifcPgTdvBowMWBlpmkzIR5om8GrqiTngfWS3VSe2RpzEwjnEbDFf9MGjN8zKjwMrrvdirpiYf+M6XLcfVoBXeMJUrfpyf1/ee2ruP2PJz6a1zMetd+AlzvlCg2CRxZelCcon+fKoWcbWt9bHF5mbZRHpVbBofURXNA8utdzKWrY6SY2Nrzu4wZDiRJRVksaqaeRA82gCrfDHcu5LuONys78Uvo+vkFfvPRXGeb6QFMxctbr62Ycnpcz3UB2mVEthdcxzx16eo0AAJQu11sd0pVaqKYlBymMeVEutJitPQwnXHVFnxfqKtRhAnItJq9CoiVPOeYVRai3SrAIWlPcHKDUaWSwHUcdrLibSOjJnhkwK98aSfIVWgyHjapy3NUWAe5pOWRm1MEMtyiAldG79UCu7M1cXsY6prJZBnTNptQguI58u++6J424+l8zhrU3T0HUtxppSxi3Ogk2LkS/XuVCwqrGQ62daqGW+4XmuaMKMd5bdttMwxk5DJTHKURNiYhgT5z5hJGNFrYRu0BPEZFBxWZlTlz6btezycL9OiNe1UKG7D6tEH9d+UAFujOBbR0h2wewQyDqZrSmm47zQanIDK8mz4E/WGLJlDqWqCQ1VuMzx0DBrQqxSYCsGP8dhVtPRCFX46bgtvvkis3SDECEZDdzPYslScUjDZqsVqs/9idBr6F1MKuSMpFV0yDI51dTWzSAmITNigiGEW9YzVETYbre8ePGKnNOFo2Txhtdez/Pk+3VC+rHmsIZUnp7r487x9Mg6jPLEGlh/Vp/xcRbb4gtZmBRr/LhzKribplEaVWsZp5F8yJgiVNu24XrXaaGOD96hvlaIvb4TQ2ToR6aQGMZIqAWGrcU1DZvdDkQ4Pjww9n3JrFSe6Uq/KiWenKqoCNQIB1hCC9u2o/n/tXd2sZZlx13/1d77nHPv7dvd0z2eOOOZCBthgYMVEhSh8CEUxZFwQhRHSCAjIlkiUl6QCAiJ2PID4i0IhOAhgKwEYkGUEJmEWJaCEhmiCAkcEkCWwTExTiAOQ8aesT3T0933nr138VBVa9Xe59zu2+Nx3z5ol/r2OWd/ro9a/1VVq1bVxnIqrlcrjxZpiSIaz+pk+wQ6s/+2DXjdb9y4wXq14ubNG5xeP2VztPIY9lR+T4LKRDbx4+OsH01r9B+xgWViF4pTlQdjMo/gZhYz6Ij1uuPW7VtcPz3h7t17vPrqHQv7erbl/DwknWj7yltt8sUMjyjAJ8pYvLbJt+ta1l3D9dMjnnv+zZxeO+bpN93g9PoRZ+dn3Ll3nzt3t7z85S1feHmL0COMbDamzRz3K556+hhpryPNhlj0rx50aXylsZa9tnI5Q9CAmLiDucqNF/DjLl35VvosgWeRwLc3FCabU3WrivmcMEsSYyGuG4tjfGLPmTQZiwqmJ2q5pJogYvjKlJmSeKYFfn10JjU5QKUsmk2kGn9nHNF6PhY5QNyvtGGfo3/XrdhsNow6llghERWtgLi9rLxzIhn4ufI5O5/bPH/fB9IPOpffl0F7P4BPzSjxWf+a0rbxF6Fju64rfyGxhquiqDKMlmz6YZPMZHCm+ukeCRw8kURjyTni/W3XWRIHH91N5C1tpSR8lmbyhtIeZhZqPbxAW+Kzh+1cfLOTNI3lM20apDMTioUUMAl8tV5ZNME2xpq9JxSnmDAnVX2QCSyBTeGx+Tgt0nIVGorWWjaYmfvlar1C7t0vkSyHocaOx5O05OTJZbE9lS/2E6gmDy3X8ru2Yb3uuHZiO0w3mxWrVct5H7F0lG0P51utmoPAeS+s+gYdWzOFiTkVFwFLY9xqqvOUT/L3APC6wB54p9ObL0lXCuC2cBL2t9ZVXJfHBCIfk22Zni5eksKUiri7jvtMN62w3rSICufb+3B35LW7G+7eO2a7PTdmbRos7iqE1K0AkgwpBTwjA3ckW647rsJDxi43Bu26ls3GpLph2NL3cH5+n6aBvt+WWdtbIeTPiUarUjuz7N5S8ZjgUwAXEa6dnvL008/sAPjogfrDH3dMA0rHmIqox5I6WgfgbCBmpiT9nrTZ7Hs5xuSdVcKOFiBpV1XKJkC7TOpzbxaIxc2yecgH83a7dQB3m3JjwDf0x2UX4z6SifFkdzKLXcS2aUN9k5QB7mq9skw4Rxs2R0cW/2SwDTdFyGhAwlc9m7L8PxHT6gyE165RRAhgl7p9obZpGrrNGmlb2lVH68Gorl275iFwbcHS3HFt7Ckhq2QzUTaN1LrmeldTF0UC39PTkzaLPiv47QdVlbt376E68Morr/KKByMbhpCKqhd+3BNb+ucgHuWLcRi5Ba6fXuP2Uze4desGb3rmtu2YPT5CGjO1XTu5xjiMHB9d43hzynZ7j+25wrimlWO65pi22dDJCqG1dQqp7ZN3q4bGkMuSwXsczfgUnzIMSNO4Hdxi4hyQG6FXvskMIrbpUnyYi1ps7+x9gqmrVtGxAolT28Jq5TnqektQeu/+MXfv3bP0US5uSN61Fkw4sQnOJFRn9eyOZ5SkagdwVcty3/cWXP78/Nx9XfvyvCbAO9u/JxQAW/25kdjgU68WsczlT926bZKhM0HEyojvUFVYVa0Jhr3usdkgADyuKed3pK790vZlpHPy8wgJe7+JBKnb8SfgQRZYpjG0s/QeAL7dbgH3PPK1lTmAS/qrZQuZeFfii8BY46CWBMMnh9XKsv+s1mtWm7X5IY+2mNm1US+lrLDJ5GNSj4jt0vlmnOxy2XVdCQ/cbdY2eWzWdGvLsHRyclLitrSteFhcreK2VytSa8x7LANQPiaz33v72s9NWDxdFlqvBUc759U7r3Hnzmu+ocV2R6cWiX80jZTogBdJ4qZpASpcu3bC0888za2b17l1+xbHR2u6ztq/W604pmHoB443JxytT9BBOR/OHcCP6JpjOlnTSofQlCBWUa45zwnTMuUxWEw9rg0Si/P+xGEYL5gM99OVA3gFqbrgkZkrQGIcY+fTrqpXIMFBRxphvbYY0Ba7t7HYHWf3SrjZInvGbmvRyeAJbcB+E1eXl03LMJVWTAJ07wFtfcecZXUfx8hEg7kueoTEqGt5YpZqJ3VN5Ugo03h28tHDlKpasPmQwFtPWDHmAekTX9E+ElhPYjkkFblcRwXxImnv+ZyUvUx8ta9zm2YUmwKZaWC4lJ6lHU0PCCk87sufuTghdZK0iWkb2+Fq1vOZOWkodsYdPtuGxm9Q1+ainc2aJohGfBMtm9KIbd4kzTI0sWIiEgup0PoCpmsWYVIpi6QRvKxt005TM0+0nS98FjkjdlTaAmHB1lJnJv20TwLPv7OmNieR3O9Zw/NwCs3I2Rn0fcN220dPVibQHVYHpGhX0cf5e+i0kfM02nFU5d5922a/as2nXj2DvCUjtjE5DMp2q5x3lqvAwj5YXWxdImzg9a/ytEwAfC6FF/OOg3YNNZscDC6P35fKyHME/Aqw8es/oqp/S0RuA/8SeCvw28BfUNUvXf7VUVLFzCGD+XCKehjw6vExjLbdHEyCsHJhOlKaACI5a9cJ168fGwgPkZ3jjJdefsl8aJuQ5rD8w2Okp8I20dgbrIOLNGpNHAtQo/vJWmqs4Hr7tFX+jlal+K33/dZyHQZvZqnbJx2JJqFYxFzdDJvbOLEDpl5yj4MjY6aYoLLkXCTmlEQh2m7cA8ATsPLfeGJiN70UkNIwzdR743zp5iiPt+UYSQfK4E8TQqKmjNxkatnRWPZN57vSWVnUjXbdC97K9Hmzs2ONPFdc2DoDx2EwM80wjvTjQD9Wv+wGigts24anzsigZlaR1vWxYiaSkpc0FmTbrqXtTMpeHx2x6gzYO7eNr48spsjm2BJWd6uG45OVZxLyQGaNYi60wCgTd7vSfoUHKwjV01o0IaBoaNmFddpj3vaR5q9ogJZMWLDgZIK6wNS45URqUSgyVprM9wkHue99HcInuaZpOe8HXnzpS2bi9GTNEUzr3t1ztucjQw9n95U7d3rG0QJidZ3Z5Fv3MGo8h2/Y6VUjCJXUwqZyVWeCmNSTSahyFhMQvyRdRgI/A75DVe+I5cb89yLyC8CfAz6uqj8iIu8H3g/88CO+f0Zppi4uaHHcgTM87ooqLUz8nFDvvMZBN4BsZDv0NKMgK1Nly8JRRU6/IU0SiMdZUeddLepmuquibVL1oCnxONSluJija92YSjDpo6hSHjVf64kpCb6YZR4M6hNbgDgO3PbOOYBPmb9+QpW8/XuAbERc0xljTt6nqfz2kRk5JsWJhD+T4G2um8pfxaxSO27WE9PGmUtncZeW3zsINu3LneOTh5tU5t5SJZRovnxi1vE6+SSUOalcQ5W8w0NB3BecyFfqE0dI4+Fa2Hpyhq6NBdTGt99LzTXrJbtQ2ptN7nZId87Xn/vNJ/soeCP6N9ZjIkORlS55l+yZn5U0LiU919uwDOOirZm/dj+MyLbn3r37tG1Dv1qxcgBfdx3373vkQk/xaPk5Q8NLAp9Uvok+DaGkCkC77TMfJxO+zNW8ZFsGXSYjjwJ3/OfK/xR4D/DtfvzDwC/zOgBc3BYdThiRA9PeXd0I64Kjex7QFF/WxnQbhNYW+tRCUKHAKuJKj0UKGAZ7Vgs0bZICNYBr6uVgeChJOwBT341hIqQ3eQYuuRTDLKCF8axuttNURBkUdGyKihbnyxCS+i5/2LQNsVRzm80mAWEFUqINQxpnCpQRO7qYFUivyqpgXDUHaa2D0eyXKc6DpslL02LqTCqp/VzNMtPC7GdsLef2AzjefkzawdovEl/sPrO6XwZIhOtcDNrGJTdB6ccAihFplKYzO/SoynqzYX10ZO5to4VFDmYXaWhlNgQDqKWxHXvOgGWoB7Bnn3D/bb7nHZvNESfHJ7SdcHRkcUZUehCL3TKqRUZUyVL2rL+Zt39qXqlmlHH0RfJxVwKv7ampTdMzC6+36SV7JuUyrHR2RZ384j1ZsBtU0WHkpS99mfvn981t2ZOGbNYbVm1HLJGenZ3xv373i7zylVc477f0bKA75vjGbU5v3WB9cmLePSWqZtYWXSQIyeCB7aBFKgdKXHb1SSG0u8vSZbPSt8CvA38A+FFV/YSIvFlVX/CCvSAiX3fBvT8I/CDAzZs35+cIu+YUkwwmYxwX25GAiongsQpsdi7vsrZxzcp35iE0Ypmpt9ue7Rhg49HhigUmqe8FwKu7VfZGkMRogQvis/S83WtSit12qUzg0j1j8fslijG7UX1i2de/Yfus0u0UwI39M4jHMzVFSpvIZntpLqmXZyTGzIM5a1D5LxZay3hOE0yW8p0B9r4z35frJbnZ9qirIUFF3PRpBSmaRrrDJvKY2KnxVVRtvwJQTBSxzX9cjXRrs0frOEDfu9nGFsHsSe2s1WvYU3GJu5Zeyl9I4Vkij2BXq9XKg10J61Vr5h0PxRveTWO4yk6aubZlbo8dchCHDOC7F5Z5mMoj+YFFup0hnwFxvKderOwezjQxrYlHFB1HXr3zGq/dvVNbUBoD8G5ta1JD77kzv8Lde/dc0+nQdsPm5DrHpzdYbY48DnCN857LXCTqPe0111KmE5kWfkTkkULJwiUBXFUH4JtF5Cng50TknZd9gap+CPgQwFve8hadnSudn7sxr/gHuBcVtHFg9Ow87llnzwupE8VdNnxUUZjcQMalq0jllNq9SuO20p4nmdEXp+rg80mi8UXPnf6b64DOesGfiQlCwC62MJduJ4PAd/vtkofL7SgAPpG+ZxLqdALQagPPZdxDk8lgMsaTWWUcq9+9Tu/LZYvF3DpBA4zpt1bwLn1a35eP1f7Oda11mEs0pghJ8RnfPZkhNU3eUjeLrTpYd0oj2CYeQsNyG1/TmO27tUzwsfgRAZWsXg6qChpheFMsjBrprilxT+btGQk8zEMl+Z63lj0+wktk0WNEiWTEipZmrs+uADXDKS8XpYyeBP6B034F6nJk2j9TpK4aU9Kc4rvudstFB+qr0jjt2s5jyZg75jgOnJ1t2fbmnWbhNywo2c3r17h+esK1ayfuIBDKk7VPtoFfKDQLJpT5vF0noelYMh7+GkngpS1Uvywivwy8G/g9EXnWpe9ngRcf5Vn2PBh7ZejHGiPAzmADxpDOHDrcXNKqN4p9mjRX1V0DRl90dCkmnt22HaMnPI5A916S0qixfVehrDaHEKQ9DH2OnAhtY4tEtvlx2pOSeDKAJZtRCnir1XckVtBjgTB2s9XOFmFnlhaxbPOIS+ApRvdcsp5vnIkBPKc5ruU+i7JkDaECUbWxV1t/NZFEG+VJZp8dvUri485788RgADOW9tW0gBvtPoa/e5aQRNhs1hYGNNcPNe1O8qRWVfWutU0zm3FkGDq2vdIPPfQj26Z3rcvNH11Hu1rRrdcmEIxDSd0nqoxDT7/dYoG8fFE3sB5PRNK4J4nv3MuajqqFmD0+PqbrzHQSyTBWq5VnrXIz3RB9YUGTCB4jTFte/6T51Pl6qpfFuCLaXm3b+S6vZNNJlY5Dm66CQJbAQ+0NEM+dL+UeO6VFY7WzmR8xbUekTH4WG+bIBTkBGvr7W165c5+h7xlGi/J4enqNZ55+ilu3bvDMM7e59dQNjo6OGMJ70Pbz02+HujmMwIoUmlnSGMC0HvEQ2aLVHbn0wejCwBsJ4CLyDLB18D4GvhP4O8BHgfcBP+KfP3/ptyaKGV7rrwKn1jDzYRTXVRDMmlmuepbmJupaxcPKI/N7StmM+dL6iF8Xqk86KjN+m5QoOkYK8+Yb43n27rrbcy7F5rk7U9M0tB5XWiSB5Z47sr/qRMqS3Wv2keYB5U0w7iySThd6d8A5gepe4PZRWvtuCijl+tK2Dtzuo5v7vsReHiujKLbhZt9MldhjV6YTjxVfTBxjtITVWGv7xS7Jpm1Nm/NHtB7SVEcPtytS+j4tzSdJ3BcydTq4Nb0rnvEg98nphFuBI9oq2rTcMzu2r5Uq4LOXL6tkv/94pSh3fV8xF6ZBNedV4WI+DYFwvj8gUKXyh8d693W09WrF8dGGo826LHSCL1KrWrhpnZoqSw6DGMektBeT+cmOWr2y+j/DkkvSZSTwZ4EPux28AX5GVT8mIv8B+BkR+QHgfwN//pHfbiLmJAuFAaUvAEo5UKQAjYGgtYOrZFVn+aB+GKG3oPlok1zmnIlHLN6x2wRD4lVRGMbaGUWbiwUlAw2LgzhYHO9Qv+PZWowg5MBOlQPr7FxMNENI+J41PPXrZHBNyAL8b9o1NUP2bKFwzwxX6xRK7H4AcDSZLhb5cWdHt2DUdyUYmisc02cwPzYDldTv5Zp0rN7jgDQm90S0LLAVabD4sMN61U5SldVymJBVnVQTZ0UqrkbsgsGAcVS87a3uq65DFDbrDeOmrwk31IJaiU+0Q9+DWjCyphmRzlzVpGzXDD7xCQEzi9g7zbti2w/lWnEz4dD3HqbeeG+MzWCz9p7+4eshlDac9lVoukJEL4z+GMZdO8o4akkrNpmAU0lKFcmf2XMINLujpNk14u3Pz9kzbfu8RTDtXHMShn5gkMF3e46WPamx0LyrjQX6eub2TZ77+qc5OTmiE2Xszzi/rzB6iGgPfmZtFW4NHus9NhE1yV+8oICX1E1soSGUGOIF+/JGoQfTZbxQPgl8y57jLwHvutRbHvj8BCJqi5LmDy4WmCrgsqCu3Vd3mlepZyKROI3OWGaNyQsNUkC8yqEx8PGNQ1rGT8VRH9buFRPAYADX1E4Kpk1dl9XGWsQK4gRo4d4rMo8e6FAyBz/BfYQtM8swhK1/5ulxIWhO220HxPeB+s5z8uSUinaJe+fl2ZUGL5bSq5bh58apt0MsqhZJPN3Xtc1uubSCtj2zLlZn9d6+mT9ynrCDgdqmRVul6ywxBQHaamo0OtL0vb+/IRa8zaNkmsBiIokXW3gVXmosltrOsVW7GG7tNTME323Pagqs/ZE/43uezC/iLRIPPqDHd8ZC1CNrrOrPm/dXLQdFCo4mazyUc9vU9YPQxnrP/GPxU2yQr9ctq7bh9OSYm9evcbRZ0Yiiw8DQ95xji9ddKkLm+aIBuZYmqUqBBgEkItVBVr28olj0kEuOFXgidmJOqaoeCiled9RSHXHH2JRQmLCqTHZpNRMkRbtcB7jHQLx5GirW1OHU+jOBNOaTUNFcjE+FzVTNJlMGfDAglgllAl77nl6DZYUEsA/A67MvBsj60MszUb18/+B6FPCOQZBNSsW0pHWg7ID67NNO2IAbm/0A3rayW7YQCUWwcIFS2twmx4FRR862tltvO4wlf2r0lY7K2A/2N7hXjo5ImHCGHjxcqth2QOM+VZPayuRtwOK1RvFYL42Eywu4O6H4Zp7GbfQFEaJbkzJU+V9otLHdu7HruMgnWvl+H8MlVt/Xv0V8uRC88yQbkrZO3DrzJCG45LpH4IhrSjWTJlzXvMbJtX0KM2GLxOaVtO5autWapunME0WFcYwt7j0NDdroRFvN74+6l0TULo2HFDBvD01fyth+4IQ3pScGwDNQq+StMpRFRaXGITbJOEnI4g8RmcDIdGFDK7iLb4sV41L1JeIkA0wnDw17WjyrcrbUAiHAPLpGcSfMR/cxvebvOjk2NRfsdnAAeAEqphtn5s/dJ53ne/dRBtB9lEH8Invsw547f0eUK5+7aAJS1ZK+Ki8e7aszapN3Mw0D6BVpKkA6ZgyuLt/fjvS9cnYO98+VYYidwj4A3Q9+KABuf44m1sZ9b9EJx9FdX6UKJxFWQcQAS8r+X5O+HaClSZ/u6RLb6hv3vIr6S2JscenQdvQaeDdq6SpiYQ3vxgdLzvV51j/7+nXaf7t9Xnk6zB6ljDOPm/m4nj6HSbljLSJS2dkuyTqYJ0KQmmdR25pr4Xq9YrXe0HRrpO2sjUbQ7QD9YHlDG3NNjtjzedyEe2wNcWAmnAhvUdvNCztpq8u1eaYrBfC27Tg5OS2rwuHn3PjgX7X2XaGskhd4ScCdzRyxSFFWpzWk9PhzYFaLxdw00aG2OjwMJkEJ1Re8aYNRM1Na/kGzXYabivkFR0D6Uk4gZ0gvVZBpB9pnFpmqBmAgYlPDer3eeZZ5MvSE3bd8BoOHiSYeGA+dfVZ9ZfLweckfpDz4PalT9vs+5ov9n+4cTg1TTF1V4w4p0UEAc5EL6C6LVXF9E1W1AS7ZxdppGEbuvXYfEfG8CIKOkd5POXPQPtsqZ2eWNarfKv0Aw9k54/kW7XukP0eGgUYHuki16xtnRlHba4YQCcA0TBcO4MWOKkInQithdlcaRrejA2OP9ltGgf7sPjIOjB733txt3ee8Hfz9A5GseHDvnRz3prjF+USk0Q+z3im86300bIfc0giwWa+5fnrN+iwGae7rArqua4TpCOoGl3ztjA9DCo5JWlULdohA11RtbmfSn5TUwmucHB97aNsNkSVr9FSHgTWj44CI85oEHkA1QQk64F4ncV3Z7UfWyIuG5ff3Y/UIugxdKYBfv/4U73jHN5WErUHF95sk8ab7lNm4k8mds2O7NxeInDykSrx10ped6yoPzaXRmaQ/6wSRfb2yTwqfPTeVN86dnJxMVE1V5fzsLv32LN2j5bt97L5/Mtunex4Gt3seteeSCvKX5cf9wsd0O5Ok6yYrBIbJtHvbOZ6f6xta2PSau6/e5XOf/JyZI/b0e9iIR6W4lUXqv5KZR0faYaRR5XToGdtx8hDtbLK3H3VRfMLYxQSIA7ki9LYQOoCMlo6P/h5n917lXITzl7sC+sVDqnSDlomumhRTmfx7BexHkwb7s56phCk8/9zXc/v2zT0DtryQUsl0X/k+FycukCPyJFPBcTa+98gG00dZALqmEUuasVkxinB/FEL7ru+ca5bi3SdQPMGqG7RIz5wuhCeF7XD5dr9SAF+v16zXb7rKIvx/Q+PQMw67jHKV9Ija4KXpognmoab2h85M0J/3fOWLrzxqkfa+yoZzeu9eqeMShQIqqg6TnwwwbO3rk9T7IsLp6QmnpydXXZTXTQr0ExXuQTQXM/JTvkYDgRw5ZqGFFlpooYOiBcAXWmihhQ6UFgBfaKGFFjpQkkd1W/mqXibyBeA14IuP7aVfG3oTh12HQy8/HH4dDr38cPh1OKTy/z5VfWZ+8LECOICI/JqqfutjfekbTIdeh0MvPxx+HQ69/HD4dTj08sNiQllooYUWOlhaAHyhhRZa6EDpKgD8Q1fwzjeaDr0Oh15+OPw6HHr54fDrcOjlf/w28IUWWmihhd4YWkwoCy200EIHSo8VwEXk3SLyGRH5rIi8/3G++/WQiHyDiPw7Efm0iPw3EfkhP35bRH5JRH7TP29ddVkfRCLSish/EZGP+e9DK/9TIvIREfkN74s/foB1+OvOQ58SkZ8SkaMnuQ4i8k9F5EUR+VQ6dmF5ReQDPq4/IyJ/5mpKPaUL6vB3nY8+KSI/J5bnN849cXV4GD02ABfL6POjwHcB3wj8RRH5xsf1/tdJPfA3VPUdwLcBf8XL/H7g46r6duDj/vtJph8CPp1+H1r5/yHwb1T1DwF/BKvLwdRBRJ4D/irwrar6TiwV/Xt5suvwE1ju20x7y+tj4r3AH/Z7/pGP96umn2C3Dr8EvFNVvwn4H8AH4ImuwwPpcUrgfwz4rKp+TlXPgZ8G3vMY3//IpKovqOp/9u+vYsDxHFbuD/tlHwa+70oKeAkSkeeBPwv8WDp8SOW/Afxp4McBVPVcVb/MAdXBqQOORaQDToD/wxNcB1X9FeDl2eGLyvse4KdV9UxVfwv4LDber5T21UFVf1FVI+7XfwSe9+9PZB0eRo8TwJ8Dfif9/rwfOwgSkbdiqeU+AbxZVV8AA3ng666waA+jfwD8TVIodQ6r/L8f+ALwz9wM9GMico0DqoOq/i7w97DcsS8AX1HVX+SA6uB0UXkPdWz/ZeAX/PtB1uFxAvjehBqP8f2vm0TkFPhXwF9T1a8+1uhjIhH5HuBFVf31qy7LV0Ed8EeBf6yq34KFYniSTA0PJbcVvwd4G/AW4JqIfP/VluoNpYMb2yLyQcxE+pNxaM9lT3Qd4PEC+OeBb0i/n8fUyCeaRGSFgfdPqurP+uHfE5Fn/fyzwItXVb6H0J8EvldEfhszWX2HiPwLDqf8YHzzeVX9hP/+CAboh1SH7wR+S1W/oKpb4GeBP8Fh1QEuLu9BjW0ReR/wPcBf0upHfVB1CHqcAP6fgLeLyNtEZI0tGHz0Mb7/kUks5caPA59W1b+fTn0UeJ9/fx/w84+7bJchVf2Aqj6vqm/F2vvfqur3cyDlB1DV/wv8joj8QT/0LuC/c0B1wEwn3yYiJ85T78LWUw6pDnBxeT8KvFdENiLyNuDtwK9eQfkeSiLybuCHge9V1bvp1MHUYUL7Ett+rf6A78ZWfv8n8MHH+e7XWd4/halRnwT+q/99N/A0tgr/m/55+6rLeom6fDvwMf9+UOUHvhn4Ne+Hfw3cOsA6/G3gN4BPAf8c2DzJdQB+CrPXbzHp9AceVF7ggz6uPwN811WX/wF1+Cxm647x/E+e5Do87G/ZibnQQgstdKC07MRcaKGFFjpQWgB8oYUWWuhAaQHwhRZaaKEDpQXAF1pooYUOlBYAX2ihhRY6UFoAfKGFFlroQGkB8IUWWmihA6UFwBdaaKGFDpT+HwumwhN6Ay3zAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(trainloader)\nimages, labels = dataiter.next()\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "2. Define a Convolutional Neural Network\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nCopy the neural network from the Neural Networks section before and modify it to\ntake 3-channel images (instead of 1-channel images as it was defined).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Net(\n  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n  (fc1): Linear(in_features=400, out_features=120, bias=True)\n  (fc2): Linear(in_features=120, out_features=84, bias=True)\n  (fc3): Linear(in_features=84, out_features=10, bias=True)\n)\n"
          ]
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "net = Net()\n",
        "print(net)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "3. Define a Loss function and optimizer\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nLet's use a Classification Cross-Entropy loss and SGD with momentum.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "4. Train the network\n^^^^^^^^^^^^^^^^^^^^\n\nThis is when things start to get interesting.\nWe simply have to loop over our data iterator, and feed the inputs to the\nnetwork and optimize.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 2.209\n",
            "[1,  4000] loss: 1.849\n",
            "[1,  6000] loss: 1.681\n",
            "[1,  8000] loss: 1.558\n",
            "[1, 10000] loss: 1.492\n",
            "[1, 12000] loss: 1.458\n",
            "[2,  2000] loss: 1.385\n",
            "[2,  4000] loss: 1.357\n",
            "[2,  6000] loss: 1.331\n",
            "[2,  8000] loss: 1.292\n",
            "[2, 10000] loss: 1.274\n",
            "[2, 12000] loss: 1.254\n",
            "Finished Training\n"
          ]
        }
      ],
      "source": [
        "for epoch in range(2):  # loop over the dataset multiple times\n\n    running_loss = 0.0\n    for i, data in enumerate(trainloader, 0):\n        # get the inputs; data is a list of [inputs, labels]\n        inputs, labels = data\n\n        # zero the parameter gradients\n        optimizer.zero_grad()\n\n        # forward + backward + optimize\n        outputs = net(inputs)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n\n        # print statistics\n        running_loss += loss.item()\n        if i % 2000 == 1999:    # print every 2000 mini-batches\n            print('[%d, %5d] loss: %.3f' %\n                  (epoch + 1, i + 1, running_loss / 2000))\n            running_loss = 0.0\n\nprint('Finished Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's quickly save our trained model:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "PATH = r'G:/datasets/train_data/cifar_net.pth'\n",
        "torch.save(net.state_dict(), PATH)  # 只存权重"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\nfor more details on saving PyTorch models.\n\n5. Test the network on the test data\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWe have trained the network for 2 passes over the training dataset.\nBut we need to check if the network has learnt anything at all.\n\nWe will check this by predicting the class label that the neural network\noutputs, and checking it against the ground-truth. If the prediction is\ncorrect, we add the sample to the list of correct predictions.\n\nOkay, first step. Let us display an image from the test set to get familiar.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"121.003431pt\" version=\"1.1\" viewBox=\"0 0 368.925 121.003431\" width=\"368.925pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-10-11T14:45:10.343963</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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qwEUVpwmnqVq9ijZrrqFpxiNn2MQ1amHFe51cTBwzeGeszqafDwHE03dXDP0NWjLLfKKUxDzDvCSESiGqHrfUKKCo+WKzRrI67k8Lrw4zWR0LHQVPqrBKcULqPKpKmidyWmDOkGccmCfuwGlGykwMcL1zxABdEKIPdWOpiRSpVEetAI5C0GJJUPfu23yZnPtnpVl1gwHeIAEY7xgz3KXAvMy8ursn58xms6EfeoPOcsaJsOmNNuedWBTG+db/pvDOu4axk5QgxZJpFEQzgnnLToolJqXgyXh1OF3MU8eYBs4VxFk+J0hNZAr4mpxSySgZ1UxRizpLsvnrCSABVFcsV0MA/IpRr0bv4qJqNdyqystXr3j18iU5J+ZxXDHrLnaEcEbqVyNdN6s1Ii6Olny3R6mebUarN62lGHZb//0uC24JzB6hx1UwREVQZx5o0mr0mqMljs4FWy/RG0Ps1R3uu69JSyYdJxBhePGceLU3rn7oUBw4W+cBu+aqypITRQteFVfhUde+c9PhugBFkeoktBlUgqc0amapxltBVChSmENGUdw8Iykh/Q63u0VDoMSOEmJ1Wez+tdjEI4iGFS0QESh2b516XBGktKv+YeNHh1BEC65NAEf1eoCKLxrEYFlupHrXlwmo1Xm+oJRdhj3OvO++79nt95ScODkLM+fkKVo/zy4vEFC8JVerEc+YAR+XzMOxUEQgBNRZcqa4SBFnP4uyiJBw5DyRJ/O++iB4Ac0FTQtdFHzwdMWt3gdiIfiZWlexSFdhhBXHf/8NftN4/0a49Zoj+C0M40VQ04IcPZ+WhdPanCNlnhOHw9GMkHD20JOF2bHykvUdFUA/5Bh/3WsvPezmXQptgVs8Zni31tR2NfC6xnpoxcuhYebl/BliEE1uz1VD2fBvLRXmU6XkvDol5+Nvv+n5oup5M8ylcDgeefn6tfHWlxnvHC+ePVuTb22JnA009Tzr58mZ8XHefRtEV418u7H1A9/lMNjqDbgawYKrUKFSxOKQlpsydpbDOdsI+7pR6zzCwyvKnJBHM+BxCAwRRDxIBDGHSvEEJ3RiLLQ8z2jJ1dtVHI5YN0IpGVJAiiI5272t1zbHQI4BFHyuNqkIkiF7RfoEFPw44ubZjntzjSrMLiA+2GZXmgG3e+VW7qycPfFWYFivgTQixAeOHx9CaVNIIo0Y3/wDrUyPdSIJdfdv2eTq0dQwc51g67DCIHHC7vqaT3/yU9I88/K7b5mnkdNyIk8jIGS1RCUaQT3TUhinTCqZwzSzZOVxVsbFI6Gjj3vER9R3FNdRcJZdV8BF3DJT0kLuRigZzQtJM+I94jqKV04lsSyWpCtqCWkNzhJk1UioqiX3LjNFv+IGv4mFv8sIf6hh/nPxaqVxaus9wiZoSYVSYEmZlAqneeE0TiwpEfuOEKNxp2tSqN3rS5jpzSKpH3oe73xeGmtEVvtkeG8zygVKrh6qYeUth9OMkGqhlOqtirNNx4NIWJ2TFQ/VioauCUyDjrRk0rKgKF0MlODNGGmbE4Yl2yk4isJc6XLTkpgW42yvnqPaHCvFIh57Vw3lVwNd15dawrB52qXY+QgtEWuGvsEIb6+7i4u5jrZhtY1HVi++XBguw/I9WWI1e9HWpPPQdyBGJkhLgbSg0yOqkMUiYquRMVLiUlKlE9uxeRWCBkDQKUL0tS7DNtFSbUyOgRSD1YNkRQq4bEFxDpC3Nhf0cETmBb84QryGTqHfgq9wVa1uLeum5aoRbwldOUNtcvn4oGkN/DNhwMFWTbCbdHHwlryrOJk0wnxNFDbe6ZpYaUbuAnFriSXvuH3xnJ//5X+e8XQiFTg8PvIwnsjHE6pCLq7CJh2qnsdx4vXdxLwkvr87Mk4J121x/Y4oA5vuhtgNqO/BR4p4kg+G4XYTKS1oXijLTM4Lx8d7lnk2zrATMguP6QEhkVSZM/QBBId3EL1x1VVTzXrT9jDqr0/G+5KY76PK/Zc1Gv1zBTqrEUlqZeHjkpimheNx4uF4Ii0LoY/4GOhCoOt7K9pwLdA++4xtKbz7i8+v+WHHS+Vj279UodR/azZ4D00VVmlGKSFFcWq1C4XKYy618KraZddYF5esK032PaVCgMW4cJoT8zyiqvRdJAZviXw1uKMVmYmAqxWT87Iw58xpWjiOCw4lOnNgcjG2lBQIle3jS9uUnhpxM/KZUhy5JHK26lO7Nm6lwVrkUAve3gvZ1TugSitYE+qmUjKCwxXb4Iu2mhBIdDhxODq8drarbjoQR1ZYpkw5HCivXqO5kMWglBJAgxnNyRXKRdI3qiPmYFFScGiDYHPhjJkruQssnRlwt9jO57LBKaVzpGtnBvzhiJ5m4jMg3iKbgu7MkK+yCxVIUWp2TWQ13zTIVKQyneA96Oh7x49swIVGxkI9qr6WpUNLErXfL0Nw4BzW0X7yJJSTuuuZwc+ktDBPI/NkXt6SM0tRUp3YS7ICgJQSuRSOY+K0FOZFmZLB2UEdUWyjERfs4T34AOJqaT8VPzOcT7N5Tc5HfNRV84JSWIpNoCAVZ8SRol2X4NtJ1GTIOsHe4+v8l2CU31fRqOt/6iFXnBY9V6kp1QNU091YlkzKhWXJzEsm5WzGA85GRNdp3o6A1SCsP37NecuvfcXbb6HlUfTi39UzUl0x3xVmuXAczpUCzcdqn1meQhNPcOM6j5srzhlCseTuwrIEcm5siurdoTjf5BisSjmlxLzMjONoxraL+PU6nhPGzTGyt56vspZiRkZzTRKWC5xbnlzvszZMubgG7xjrBNH1323jWCPnBgMBa12GAuJQb/i2+AHEQSxoUDQEY6SIOYAq3ox3aLBFPn+vKqqVUYZQoke9s6LtKsbSrEmJHukjFEWltEDJ4L5OoKuOZlfzBV208Nm7c8XrG46GXESOdRqx5vLkA+bxe8aPasCLOJL0lhjIkZTO4aNzyiYWq3iTRKyl1NIMmRY0t0lgCYp1IlVvT1VJaSTlhW+/+gXz7FiWzMvXB6Z54dXDxMMkjFPm/nFmSYX7Q2acygUGDlOJFBdwcYsb9vhhwHcDvutxoUNCnRSVex4kUrJnniCnBS+B3dVVPbdEyZl5djweHkgLHF0mSmbXO0Q6+mClzJ1ztTK1GQnz6Ipcyn+dh1WK6Rs0vj+fIe8yhCLr16zm4IJulYt5iqd5tg0zF+YlU4oyL5lclHlJzHMipUQRjwQhFZgW80z7WMNabWz8Nb76sOP+Nef0rtcHUaIUC78xyqAX86W0JPKS0JwqRc68aG1JP5Ga7MzmVdUN27myeuCUZI8LI17q+4sWCplUEvM8kVKm5MTxcAS5NLqGg2xlR7/pSSVzOh45jhPfffsdv/gn/4TN0PPZi09AhIyijpo8NHgnqKsApSVUVS/EsiTjpdRNY0aLmsMifsXaQUl5oeRkWkVvXMtV1qEUgwGLnnMYza6ilcoLjW9PKSZXkDMSO7i6QkJP3N6gzoNMFEnoMEA0E6bDHmJHbln/nJHjCVfzCPZwLD6Cc+h2A71JazjDOEhq/G7fRXwXrapzWtCithVYCIIMlbc9zrBk/HaHv35mxzp48JVdpZaQjc5OuBbRo7XOwY7JaIdGW26PDx8/vgcu3tgcJTCXgGqmZMVJwXmhiC2AlmL0zavRs4cjrMQeWD0ZG6UYeHU8PqJ8x5Lh8ZhYUmacM3MWxqQ8juYJvnqYOI4J8QEJVupbtOqeuICEiPiIeG+UIe9w3lejUj1P5yhA9o1K5AhdZ6W/iyPJAskzZ2HJUJKyUPAiLKkWUKirN7fUoiRjpJQGRbx5JS+eakaOep3ee/Wbp/zr7tLFh58rPeH9AEYFN9REueaUmZalXvNkBjyV1ROfU6bkXMuLbdPMuZC9rh57UXu4S68fVi/5jSef/KgHjvmg8it3gEtqoBOthvmcmzFDlKsBPfOwzwdQ+eMtPKZpnzQaob1mdTbaxZRGqbRNQ7WQcyLnzDwrKSWDDdcYu3npPc3DTJU+eDqdOBwOaCmk23yh5VMNZinrJnvpRTfsHpRSHKU4O9eqQ4T68/eWlnwtK17+1rjAx5sK55OIquLhDXt3AqVCpaIFVxR1DkJEug632aLe8mJFZ3v/Mtu93ZsBVw/ZKZKyYddLOutuOE+OEbyHqy0M/Zo4tMjEthIXA9J35nKPM5oLxRkUJt4hsVqjfrHv6Qdk21vy0svFnGxVom1jv6SCtk2lWbCL6PIHjB/VgOcCpySQHaccmUvH4fDIq9evECk8u3YMvfBs6/hk5/BAELeWObQhwmootS4GGzUEQpjmiTndkbLwMGGFN1msnNeHelOKhU6acDHiOzPguWFkMayeTOwDXR+J0ehtKWem2eQgl2UyVbaUiVVfwQdLxpW0mKKdKi72OBzLfGReLOp4OC4s0bHvPX3wa2JsNRLvSRjlXFiqolzLfpvXLOfwXM7PPbFxcrmoVlvHxSvWjfLymudafHMJXXkJeDG4JJdCyoXTtHCaZ6Y5cZxmStFqVCAle41FJQtaCktKHI6OLgZOxxMxBJ5d79kMA5uhI8RQDeqv2X6UN0g7b8Iv7whdFVzJuJIoJaGakJKMtVASZZlZ5pmyzJCTYaHJONx4wQVHKdmYVVqsNB3zyqUsdvvyguZcizH1yTHVmJ6iiSXNLPNSDZzBJSGGM0QFSAhIjKSUWaaZsiREwTuLV8Z5Bud4dfca563UvgsO7x03V1f0fW+FP7XaOafFvHMH2WEba54tT+tAXCCLolX3R/MMabHE7ptDznPrvEnYk5db/2rctTJ70oK/v8ePM/nbr9Hvv0I3O9N+ixGt10iXBLVQzy31812hSEaWRDhMuCUhzldNg2yoisuWaMxikXvNP4QKg/iSCEtGc4FpRkrBdR7tPCRwh7MRFkDyiTRNxnjrzZ7oMFA2WwRHqXoqax1Bm5haaYpyEYz9wMD5xzXgKpySoMlxLB1TGfjm/o4//JOXiGS++Kxjv/XAhpvdFtyZytU8hxa2gmlTnPVD8upoqcA0nRjnQsqOwxJI6tDQQ9hAKLiuN09pGBFJuC4SNxv7jMUqwQjRJq8TYh8ZNh19F+liZJom8rRYSDkdSfOM8VqNyhijJeLmySiMWhQJA046pnlhWqzgYpCZOTputx3bzrijvtU8X5RnvzlSzszLUqMTe85dJJzW53xTepRLmG61H3qJx118zxn9vMRAC6km1laDosYbKmoSoUvOHKeZx9PEaZo5nKazsiNUY26GYh7nGnJn0EzwjqELxBhQCle1Gne726zaMmd89Xykb453OdzvNN7Y4hLNSElQFoM68mL/zpk8L6RxIs/VcFFqdkuQ4JDscFrwatfFYxuw12yfA+hqwIVLYRhzxkqFNBJpmZjnmXleWJZEiIFu6M17rt5bTaOQi7KME3m2CtDgzEsc55mkynevXjGn2Wh2wSoFVQvb7XYtWSslkxaj3kVngl0lFzTNq4E18a1CCWKMkjTZdcjprWv59B7oSh1t0ds6zerzlGIVomnB39/hH4/oN1+Rv/kTuLpGtwHtBxDLOZnunLFK3GLvzyxkFtyccI+j/QwtV2XvUecQCbBwlulwjrDb2GabMj4no/xOkzFVdh3qOlgy3M8GvfQ9EgOaJ3I6goDrBsR79NkLtOtRDwXP6nDXSNrrGUlwlymEH2jEf1wIRRz4DhSKBjKerJ6MRxSW7JmzIxVHVmfJGLnYqvTSXWy7nHAZrqVKoZqzMKWqsy2mxyuhw8UerwUfEp5CCIVYBB8i3ge7lkUtLK7aCUavstDSSSR4R3bGHin1eLRORvCg5mG2cPMcUmoNPw2fUwpLskKSJStzUjqnBC/NstKgs8th4Z95tWuV6rrX25saP/7SsV4/5w34xdaWvP38xU/F5FiXZQbOgkstYbmeWw21V7Gm4HHrfmLRjYhtt01HQqtH0pTb0GboyxkKkMvbXz06aQbxfJHk8oK9a2G8ZcObk8CaXDOj3n6WWoCGSR0AvsKuriiSq+ddqZ/OCU7V/laqhkbdpKRJRoh5ZFL/pjkZZbBes6YnjXMrRr3kYvmZOfN4nCipcHgcWZaFu7sHjqcRtyQWteYTWjLH49GKlBzEGFjmhf1ux9B1bIcqLKWWdC8Ns24QSVE0K0rCEyHVC5cX2+i0JQyfXtzL3EmbW+scWWeozUunis8ZlzLMM0wTkjOuGfdlsXXiFMQjRdDc5shiRTWu4HwtPHKKeqU0pgdYmb46ZF6MJnhhwPECKRh+npoBH+1+uYy4DHNB58UC4uqxa0rkySSetWBQyjzbXKBSYAWatr3h42fiRYtgG2Plh4xfa8BF5N8A/ofAN6r6X6/PPQf+beD3gD8G/iVVffUDv9uw5P45JczkU2DOjsVtSH6PkBmzR2bhtHTMKSC+UHwynEzNyxYVnLYqSkuyoBhtCjgmWBROs3Aci6mD9YNpE2+vicMeN2VmHQlLYWLEzQviA77rLBHqO5vIOZGr+P0yTSQn+M3Atou4kpm9R0rGaabkGSke59V0PpbFPmuea1InW7lzskjB+c6ohZNRwe5OCecdux5irAS686x/OlRZcuG4pKqkFyv/3a+JzVIrPLXOJXdh595G3y4Kpd49KRBgmkbu7+/w3jMMpokdfC3YKJmcZlLKOCd0wTqz+GgbdjuVlDJLypRcWLzpT1vThLSq+DnvSKpMKbGUUtUrz9xwXdUkORvrel5PLtflKenZ1j89c1sUsYa7GfCt2CMnvBYChahKr0KnMCAEHCFZla2gSOVNB1W8N9jPzYZwyzIjS0Lwdb62zUbQpZAzkBJ9dHgXGWRAnGecZu4PJ+Yl8frhwDjNHA4n7h4eSUvi+Hiq8NPI4TTaxl49XZO+xTz/NBGD5yeffcbVfs9Pf/IFf+n3foeui+w3AzF64lJIvpCTzVPNBq+UlJGhR3VnF2sZzXCm8a1raZu2t3lYgSRL5Ns81IqBC7YJhlTolxl/GNHXD+SHB8K84H0w5+ThkRImchhMNwVnHria104p+I3H76rT1BXbWcmoFJwKYV7sSMYJ0zutFbcilMeAeo/kbJ52yejpRC4Zv+/x2x7NSp4wfr8H8ZBOR06vXqOqhM4glBgG4tWEhFBhG4dVV5vERqosfIdVV6diEWu+iGY/ZHyIB/6/A/43wL918dzfAv4DVf07IvK36r//tR/wvTbE40Jfd1WpMpEBXEQrob8gFLXqyJWMATTKD3BmXrRsbv1dCyxFmRWWrCzFVAo7Z4lIHzpC7Akl4UO2xRkSPqspnTkrjTe2kdjkb0m1asylJl+sO0jtumIuKMY/Mq8958JaXWcHvXrzYDCLFruRkszDmnOhL5VguCY83n17S00Yiiqhhtj2OCsdXnrfevHvJ3ZM3zTm7x+G9y81+dXVEv9zLNgSWA5qo4723azH0/yzIqZCqaWQRci5JoBE8V7W6GH1vgUuqzKfGmNZobN3nst7jPf53W2i1cKTVXCteucVq3VaizzUHuSy0vtagtJUBBVypf6pQs7m1YmJVDUP3DwPg8XNAzdmkQ9GWZ1TJmeTRh1Hg6NePzzy/fevWKoBT8kS9HMyCuJcW8E5MiIGh6TpRPQeKXDYH4yt8ukn5NwzxGAiVcE6R+VsRlxzIs1WnJacUlK0ZHua14jhzanZ8i0XRft1XnBeyLBK3YoqLhckZWt6kjMgOB8oziHZxKeRTLXM67XWlGyT7cwsKkrxdQ2sE9aur9b7JqXy8Wu1t1XqOPveZI8yT5ZTmsWsZQGSGBtGMypGP84pQVGyeFyGkuo9ziar0E5U6zFrXWirtsoK/f6w8WsNuKr+30Tk9954+m8Cf6P+/m8C/yG/gQHv+oGb6y8svPvmwClPhG6g67d4L3z2xXOu9wPPt4nYL3hmSpktjFzDksrtFIfQg0QIHV2/QbMyvn7kMC2oBFyMxG5gf/0poevxcYOPA7nMeJ/wOdX6CqOLWSu1M1XODIMjL4mX373kMXqCGI+7lIL3nth1xC4Sl46UEqfT6SmTotjnOOcYoml7LCiLFjQL01JIotwfZ6DgpON6b5uc+GRZ8zc7dogYHNSb/rB6T6keuBm+Uje5algrFrdugM2k/prZc+nkajW2JWdjCrWuQhfJu74LhOxIGYREo1oiBlGJCMuSWZZU8fBqoCvU0DS3nZPaQswzbHpUTLIIylsGukE/7ZzWakM+bGPKKfFwf09aEjkva3L1dDyyLAtpWSglM40j9w8PRO+ZOmvvV6oh887Rx4hzjq7zhOANLRztqKZ5Meio1hOw3isqTJQtf5BMzTuTESecxpm7xyPjtPDq/sThNHE4JubkyQqu2xB9gViQbPMt1krP4Co0N5441VLucTImx7fffs92GNgMPafnz9j0Bqlsho6SE/N4pOTMMp3Iy8R+v6vwIaTpRE6Jx8PxLfPjxBF8AB/s/FyryNXzLlwnlqg5KrMXpI+kT18g13v69JxYJjLC5JxRaKtBNxEpo/SxmAfu+oDbxCrqtTxhx3g1yEUUklpMkEVZ6iRpchahKCkbM2oaR7QUwhAJfVxhWXUOf3WFGwboIkPobYMPRlPk5oY51uRplfi2jb7mfp4AnSZ2l2GFCD90/KYY+Oeq+hWAqn4lIp/9Jh8SY8/N81tA+ebxG+RY8LEn9hu6GHnxyZe8eHbNtXugk9e4ckLne4oulQDvUOeN6I9H6UEjPu6I/S15yUwp8XhSYj/QDVtCv2G7f05XkyEqniUJ3o04Z3xycpW8bBiWr/3rVHDiyClz9+oVTmDX92z6zox37CxhGTpiWEhLZhynJ0Up0kJKsc4v3ouxGbJjwQy4o/Bwssk39J4sHnFaQ75L8Po8JERct3kjwVWnRy1lb6XgQusReKblPZ01b/vmq7G/ADRVa7Pf1fM+e6reQR8j2RcWa+pZw0hTa+v6Aec9y5KYl2QblLOElFTdG2NdtIbB9px3dn6VZ7P68e2YznCQHVPhHRDR+wMZcsocHh/NUOdco4zENI4mS7osaMnM88Th8ZHgPXOw5gDzPLPMEzEGrnY7gvf0SyRGbwwE17jTdUN1Huet3VabY0teVsOdhEqtNMnZ07zwcBgZx5n7xxOH08w0Z+Zcw/EQqpeoVpxST1PExNSCE0b/QJomtFgFbJoXXn7/kuAc201PSYnt0K+PkhPzdKxJ5gNpmZimkS4EnBOm05GcFo6nkQtPxy6zc3jfKqzN4LZCpzWcah66GkV2cQJ9hBfPLGnsHL1zT7TNizMtFbyzjjZ1zVIUFwIuBhRlKsb2avi3U0GLET+9WII/OWEJNjvaZp9U8RXmnMeJUjJdDGgIZCckL6gTQt/jQyAMA323N5fCmWBX3g0s0VfLbRMzrqcuK9bdIMxGSLos7/qQ8U89iSkivw/8PsDNzc3TvzkxQXagGzZsNpnNNLPdXdHFwGa7Y7PZ0UsiMlpSYDFhHF1lI63rh7jAsLkhxj2xv2LYvWBeCp+lnmF/srZnCl23oYsD0ffUNofGBqje9SrgTmMDm3eNUvHvBdGC11JborUkxHlO+uDpuo6UMzFGcimGddesWxPZ8t5ucIhWgq854EOEkllS4TRmjmPgOCaiFwZvmKK+QfZXVe7u7vnlV39WaWsWvro6SavapXmy0XovDjGwicF4rxdT5hJOfvIdb+wZqlR2TSSE8LaEJhUCqUk4c7rOBjx46+ii3lc6sKzeaINJRM4FI6XpwTRRKNXKVmlxuGGuIZhYUReD9YxsK6Sa8CdSt+8YKSUe7h84xbF+p0EI8zyTc+Y4jiyzCUQ5bx10ovdGq0wLOS3EEFhyJnhvwv6hNjiuoXRrku1cMKMrDlfPfU5LFfQSsve2OboCLnAajYo5p9zsFYhbk+1NrrakcmEi6n6l516p3nvEC/tNTx89m+2mwl+mZZ5LMUaTFlJaOB0eTNlwGklpoYgnDg84EebxREoLh3F6w37b/PPeWaKvtiaSOnma2BeijQ5u3y+1L2Vnm5BivR1SgZl5lQUr2Ntb6ba0zaAokiyCm4vlHFojFV8XqUNqUY3psOQ6uVejWiyC0VL7SiCoSnXEYKnfXYrii92DbuhhjY4FQsDU+ZrD9YYbceGEaVtQDUTRd63Ad4/f1IB/LSI/qd73T4Bv3vdCVf0D4A8AvvzyyydH5pynG7Y457i+VbLb4ruBop4YPZ98+jm3N3u2JbIrQpkj4/wdRRNNYUDVo6XDu55PP/tLPHv+Jdv9C26e/ZScCy9++j2n44nvXr7i2+++x4fIbrjFh8DjcSRNE3lRXPE4LQTnid5b5ZVWOtxi5fXzNDKNI15gEwUJ/knIU+pM7IcB7426lLJR7Y6nkSVlu8HONojORVRNXyLFjuA9aZnJaeFweuAhjQjKto8MnefFTWToAvrGbcu58Ed//Mf88bcvV91iMC6wE/Niu0rHe/HshmHo+ez5Mz578cyw+xVe19VDqLOqevOcXTlY+436GIyG5j0hWhTS4B2rPvR4YDM4uhhZuyM5Z7iuOJI3SpvtMmbEUu1ruOTENI0m0FQlENIyszRPbFlWzjtibIvtdiCEwPNnN+z3e+s16d0TGGU9lXdEMqdx5B//6S+4LII342csommeyDnx6uGeX37fJF7b8rQtP4TAbjPgvfXWDLVZcevM4px5nc57o7iJIC6CCEtN6krwSN9b0UmVb3g8jtwdRpYlM6VCVtv04tC1GVjv2UKuNQHSIpG8WLJOoe97YvD87MvPuLna29/SUmEgYxalaeSomXEc+f7776zxcS6komxeH/n2fkREzCvOiYzlrC6HD46u6yBESrRo19ZutbiturXONxGBEHEIIW5wQFoSKWVmnTjgyHqOqSwXVTel6smXXCizyQ3kiit7Z06L0ffsPkXv1p6jWny1VRY9lmTdjIw2KaAOnxRXrNAnW1KH6AoeZR96trvdCoiIggRpFTysUcaFDZe2ti4C4PfpGf2q8Zsa8H8P+FeAv1N//ru/0ac0poQ3L8L7QIwdw2ZDF03IKMaOUCI+R5MCk3N/SjB/LBfsIscN3faafnvNsL+lZOVqzMTQczwtxPCI94HgA86ZAdHm7lF9cGmtk5pG8vnCWgLJSiW1FlQ0EXq5UEf03qAAy6D7Ncl6hgLcegNVtb6GWtVZmRgFcrKKxXE2SpnlRS59qzaU0ziSsrSZgEWYZmBi9HR9pOsiQy0Rnq+WGhGsb3mSWEJb+N48pAaTtEhDV0P81Pt2TyR/lYotrvfMvJLmGTt3rk07txQ1LzDnzDIvpJIZp6l2T5+Zp5GSm1zouQqyFfjEGFjSvt43OZ9QvdWr2X6HK55z5niaKiXT1TnSilCUZTFPXFxCkt3vtflwFSQKVRLXO0dczCGwrkw2x3zrcek9LvjqgVsz2yWZRowEjxNvuuKiIJlxWixnkPJZGpanjKHLJP/5tKu3W+ehMZUCfdczDD3LBHNaqkNr0U7rpGOb58y8LCzZ4AWVGRfNgC/zbIVLPhLiUwPe+nS29j0ml3uZU2oUwgu4p0KEUnnsJqxl0hYm4lpXqtSc5PphZj4bnbbxzu04lAuHH8GkGdpu3lQDG0ki189YJ/savZk9z9Xzd1ZPZFBl1UNak9dyeUa/Kubjwvv+4eNDaIT/B+BvAJ+IyC+A/yVmuP8dEflXgT8B/sXf4LsRcThvUpGnaeH+4YDzns9/8iVdDOyuruk2HWE54ZaOkiKqYlxLi4tIC9wfZ0IMJDb4zXPc8AzpbpBciN2EZk8XHomuw/lA8J1tFn4m+kBy2W6swGYzELrIYRw5PT7aZHaCF0/RwjRNaBeJ/Z7N0HNz+5xPPvt8xUpVldjpWm3oHh7tLouxDULsiH1HKYV5scKV3EI8FwixR8STppmcYVo8L+9mhj7TBWFJSuwy/f58HY3YUFg0rYlFaMpxjnkWptEqG4cQSPPCs6s9JTfueDFnobTCKIdz/uyNyxrc2fe1yRMjw3aHiCN4v0JDDQZpUjXWreS8OEEqbn42OCktPI6PpJw5jCPjbBWPx+PBjMg8m9BYxaFLKeS5nm/JlJKJXeTmZk/fd+y2W/a7HRIUVfdORsq7Fsy8JL55dce8JLyvtQBaGbpaWNJM0UwIkdhbUY33lkhrRSHeJY5LvujuLgTn6HxoZsbmv3OIrxuai9U5MQ16FwJ+Doh3ZLVqzHGaeHg4VtGqYkZGztW2peqZTMvCPKfKejKvctsFuuDtuDe+wj6FeR6tyO10oAueZRPwruP2as/1fsvd/T2v719TUGYxVcNTFsZ7ow0WNXhx2xd24alMsW1SVuAUOlvnS1pIWdbGHGvSWWp8tlYr5oqNJ5SMSkbEJAEbJCJoTe8oWirbpm0aAlIL4FqzaKGyxJrHXyULWn1cadK+lKoIKzR2UGsYbVIrNo8DrjZhMIXEJzCjKqE5U9Wou4tJp+uvevbc1/9++PgQFsq//J4//Qs/6JveNcQh3kLHeUkcx5H91RXPnr8wY7PtiMHhtUPKmeNtjmNdClk5HjM+Jop0uG6P6/YQdogUQjigQQm+x7toRtIFnA94F9bikWqq6LuOKDDnVA1DFfFxzjzXJRnfOUS6fmC333Nzc8uyLByPR3I2gEPBEjsX3qiIUcK6rifnTMomgtQmneHF1rfQ+Q7x2Qq/jok5Fa625tnvrgodTw1SyYVc0kU/xmrAncEHs0CKgePmCKVYiXYxRkqpE7l5J+ZJNmnfMxPnIlhBMXpX1xKP1BC4LuAWEtp9lgvIooENTw1oypnD8cA0z9wfDhxOJ+Zp4nA42GZXZQqsMtDoemkxqV3TDFnouggUhqG3yticrREIrMckb3zvm0Z8yZnXjwdO00wXe0LoaLonqoVUFopmul4ZvEUfgYaBW1JXgLCkc/JMhOA8fbRimVIsxMc1R8S6mIs4cBGRiAQIJSHOkasq4TRNHI8j79IdUer9V3MklmTXJi12PJuwxTtPFwK73tfrYhHFMltiUjUYxh09w9Dx/PktihK7aOJiaphxyoVpnOqtrV3eg7J7c3nXCMt5wUeDgYo6Su0q1aKwNm1aAZXFapWS1KR3m5qj1KI6TG658lrImmuke44Gfctn1YpkJ3LuilR10a0or1H4qpPS2MhgJe609Wm/2yKolIKqpVJaKLsePxUf17pZXF6Yi8CIN/7wA8ePXIkpVeHMTjlnW6AmIK92k5w3Y+stC+y9NwpPTcwFVxvOaua7b76F8MfcPJv4XPc4YB6P5Gkkl0RjHjXh9L4P4DYohfBwlto07rcw9J2F8sXVXIlhsyLCZrNht9+z3+/Z768Yx9EYJ2pFKVmNipRyZslnQaFSoQFVtSRbFbQvmiyjHTtEPKHrLUnirbIMEaY54aXQpfTGZRSe3d4Srp5RcibNlnoJ3uhtDcYOwXN7bR7qdrM1z61K6K48ZzVopEEI1BB45V7Dei107R7SGrfWBf2GqHHzmFYL2jYFWvbdkNCstUlB5T6vyG4p9H1vkYWKha25sExLDfFH5nmkHzp22x3DYNCbiFu90+bnrIfwnqEYTJCU2gGpGamqI+dA8MR+YLPZnQ0GMCu14a2pLgJ0IdQO6tHKrBFymsklV/E5Y1O52IPzONcjrjM6qIuoODKY2p8Ya6VJJKNlvbdqeFSlqVU/0htGKyJcX2252e/YdIHrbY93QghmlI6dr2qJmAZ5WVjSDeJszmyGgVyUY17IJZtuSp0DVHrqW9CtnCEUV6MQaj5C1RsuXSG+JmBl9QSVYNe0wZ3HRUcQoa9Voet8omLGWjdFVUucVsjOi1sT6b7qybd5qisEZYlym4u6bqyNJ6A1wqg5WHMcqwF33u6TD97OQZprcjbil7FrC05ajPHkWtE2sneEir9i/Lg9McXhgoWhpVA9h0xKea3g8qEj0tPJgMs9XezwqcM7k/nMTum9suSJP/oH/wX/6I++4ae/+9fIXBFDwI2vIU2kNOGDVEZTwbnCbjew81t8hNf3Hp0Ty2xFC8EL+922aoMvpFwpYFUV7ubZLS+eP+PFJ5/xyaefcX9/z8Pjo+Fn1QOal8WggCXZ5lSsca9bEs4JfdfVuzZVqCIwlLAWTzgX8FJwklDJHE4H5inTP5+fWCLvHD/76U/5ye/9FfPA5wXAEmjO12tt4WUfA945NkNYS4mXaTqrzpWCE29JWHGmzOZ91Vw2/N16DSrGAKrd0lti7gIPd66xL5pRr5NazsUVuT3UmjwkNfy3kw5Hz9XesKKSzscWJFBy4XQYSUvi8fGOh8d7NtuBF89fVNGrDU7OcI6xbZ564O+CUIoqc4Exq+lYqJ1HF83L9hIQUbb7a25uXtjczbmWnTvSUkh5Yay0OtlFfOyQuCFsb2wjPh1JaV6b+zrvCcMeFzq8H/C+N8y3WI5k1rXnAKGreHyeUU001USwjugK+GB5Dy2FFAzG+fzTZ3z+yXOuths+ub3CO7FoomQe7l/z6mVgnkZef/8taZn54rPneC90feD65gofA69PdyzLSFYqzmyeP7AawWZ7hFqE5B3OCyEaZ1vxdV4Horc8knWoV5a51HV2ViENwZL7QQuh39hWrE/voJx/XY20RT5uXR++JdffvPfK2hyjGXAVKLUP77kYTdcI1eMq9GPzXJ2YjO1FNx2nRtG9/J/9rRrvum7WOVmjlVVs7gPHjy4n25J6qk2XoFxQxmq59Bp6OxrVrxbmWnLI2YSap5FxfOB4PDBNIyUH/DIjaSalhZzTerct7+DwXSTGQIiekD1OzPg5Vylp2RGCle1fGqYYu9ok1uCVhv0C1cs+RxJPcoP1PNt5tZvahN1NmtY8UJ8DnrKyHEpNpJY33R2BGCPDMJj3X/HrrouGTXPe4btKrQsXLcPWRFHK5JyscW/1wEMNdQvG2sulcJpG4zLjsB5WrMbaueb1V/ZHTZq1ayftGlb2xRlmqZ6Sd0TCGvb6VqHpzUh68QQXzdPNSvKeJQ3My8TQ9wxdT9/3ayIZrRR0atb/1y6Pph9jIZ7JEdScgLmGQKmed8WfxZlGj8hFUrwlcL3dyxDxMQKCj7GCAvY/60ZlTQtw9VGhwlIfuVwk1XlHTqLiECJWMKJq5xFDsYbGXWToTHxt6Hu8F1IWg1ymns3QN7ygQlXWPALVNekpWCWxfX+F1YQ1mnrrSsrTB2JwUqnHuXrPZApWzbjMc80Hxfqzq0bNme5+NahnDKIZcburl32b3mXA3xyWyHdrTKhAWQ1xo/W1WWFx3MWKWn+cV7g8+XF2GWR9ORUCbvdrhVhp9uDDx4/sgQsilfhe5TDnaSbNC6myOrwPlNkxzUpeFK2l9sKMR+k8XA1Ct8D93QOH45HH159w9/o7YvDIfAd54v7+nru71/TDwHDVE31gs+vZ7vfgCjcPV3RjJL0qZC2m9x07444PtoBOxxOv+juGzZarq2uurm8Q5ziNoyXZsnkQx3HidBo5VW7susCcswqxJZGLgBSDUMo56dh1rgoJbYjB4yQRSKAzjPVv77iWTWyIohcNdlmLFqILiJO1AENJLIs1l1iywTqPD48cj8fK3zVa4NXNFf0wcJqs5dnxNPLHf/Kn3D8cakWk3acQ7Gf01nzYe2fH7xx9ZxtdCIGuMzbMJ598wjD067/7LvD8+urciV1tfvjWcqDqpThxa0J53m3IuXBz2nA6XtEPHS9ePLdK2BigmFe1zLlyxC36WBfSO0y54bkdPjs2mz2bwaqCY7DuNOP0SE618808IThaz9KUEku2hHW/3eOc4+rZM/b7PbHr2WyvQIRuuyOXzFIjNNvArZVfrk2uc7FKwlIKyzyR0lxzMqY2qGqcjJZcM3aLGT3RgifjAnRDT+c917st++3AduisrZ93bPotzgmD92y7jsPjA6fXrzjkwjLOvPruJbko285kj0mJ08O96Xt01h0n0yQw3rG+0RpBnptZeAFxnrwUxvlEKYVxOZDKwng8cjw84sQxdBu8Dzy7fcb++sbOq27Kufm0WpkmjduvFwa8GUbeDeudFw5niKMaW1c3z6cRxRmKXCfQxUJ09Q0mnXB+vcHmtvmbw9lwlLrhVOfPoKTGVPrw8eP3xKzbc8OG26NhYa5650syYr2lLfx6k4JAHwTjb07MozKeHhlPj6QQYH6EPHE8PXA4PZheMBlxSuwDw7ZnWAaG7UARJTx63FQxLgJas81Fhb7vCSEQY6QfBvphABErsa4a36034ThNRvdSzh4dFQfP2XonVkrVWlzijIniihJjrI2NLVlCKaTpXDj01lCq1sbFpKsutrEgfP1pBjxlYzHkJqJTlNM083g4VoNsmHy3HXCx4zTN3D8cuH945B//yS/5/uVrirhaeHE21jEY7zl4Txcj3js2g3Gz+65js+kZhoHNdluxbodIJATHRno77uZD1UgLqAnLXIuTjNq3RIObus4xVJrkfr+rhUNNDRFb8KJ4XzcGzp7jW8tFGrUVQuzp+sGKdYLUZOqRVKOolJJFUtURbBW34hyhFjj1my3Ddm8R2zDYBlG6NSmZtUaftVWJ0hoQ67oOWmOH1p+SFRSqSbyaoHO1u4s4Y3AEB0M0+MdkjwMx+Nrz0zF00TZbbB05lD52zD6Qk3UBcs68b1WBnEnTiGiPjz2IUqTpjbxrM9SLh8UcrUF11kyaEiknxunInEZOh0cOD/fWmnCYiaGjbDd4vTJYr2LbiXpPVS9yN6wGts2fS+/WjuftY1wx7xYIarVJbU3y9L1PPuOy5d7ZyV6jAWmRdTXqF9B6fe352FqS1f1AL/zHN+CrwbEQMTrHpu/Z9D0x2gR7nBfuX97DckBOCUnKPtSESFWr86XgXcFJRsvMOFqZsy5HKDPiHfvrK4ZhQ4jWUceHSOh6Yj8wbHYUHMNwYlms0m3J1AVltySEwGYYaojuTewKVkOYS+Mv10cpK4e8ZbubJnIpJi5kuJevmtcOqGFi8aRaKODJUDyh95ADIbxRiVmU71++YnG/MO8sm7Rn8JbsDS7Qh656wxHnPKGruKQURJIZ+tDRb/d4H+h7K0bqNjtCP8BpYk7KOCXu7o+8ev1IRkgtu+8N8rDvtErLvhqI6/2evuu5utqxHQaCGKWuD5HOeWJtReedNkqsrafKxVVVymLKhpdze20i4aHrAs4763k6L8x1U3V1k3DOsdkUy4s4R/CylrRfDieOLvZkSq0XqBFAtsRhe3lJhWmagco+Kco8L0YdxbDzXOA0LYg/EUJaxdTEW7Riela1iKS0cL5VGOsK0YRgnXCc2nW2RVMNeIW4XJVwcM4Zh7tkOu/YDdYYpO9M9lhQkwPICacZ770pZZaCR9hud9ZCLMPr1w/EGNnt9mforhTysjAfj9Ytp3a4yTm+d4mvhkwuuP5FV8dnHE/My8g0HpnnA1qU0+M9gjCfHrh/9R2x69htr/EhEIeN5RWc0S0bPPgE2G7+syrQahTOZfXtpQ5ZobX23uZUXfYKpW0ICE+7Ir1hyOTSfK8TtUJaT6Nn2ytqJqHZjpog/tDx4xrwhmMZ/o+gdCFwtd2w2QwMtRv34Tjziz97ieQTm5yIqviN0NfmBMGbkfXejHjOI8fDa8OT0wFKYrPZcnP9jK7rCX2Pi4HQ9XT9hn6T2e6uEd9xeBxJyZrt6rSsGKQCXYzsdju2my0hxKpWKGu1ZTPcKWeWxlkuZyy8NGZKlVgtxQzMZruj73tsW/ZQFE8kJ0ueBPGQPUUjpEwXPZeWrJTCV3/2LX/67YmmPY6eFeiiC3QVEumHgRA817d7rp9dEaNnv+3w3plOTBzouo7t7sr0XfqIDx59ODLNhcNp4buXj/zZN6+txLmuk3oratRguGnf2/375PY5++3GuindPiO4wCZ27LreOs9XfLJpfLeEb2MlaCmk2QyzRTlW2BSjGe0QLNmVc+F0OpFz4Xg8MY6TQTmdnUNKhX4wUawYA1ONmi6Hc56+36BebaMXB7WgRdfFJaSUOR1HgwDGVtxjBV3KWXfjcJqYlkIMkWFKVXp3IIRoMtMuVMgsr/o0UsXHvDgr39RYZYGtsQXUgE4a0m4eXNd1Rh1V09Ppg+N219NFx7bv6LzgtLBMIyJQ5konrOG/E8f11Q1d7Hl8fOCbb1+y2WwIcbDooGqdL/PMcZyMDbbZ4mMkbfunmIMd5Tm3UyO1li9o1NB5Ma7/NB8ZT/eMxzvSsvD4yn5+22/o+4HtZscnn3xB3w/cvvjMakS6gW63P3vFwgqtrBNKhJbCFs4QxnqY7my8z+0CL9gta+u782taI+knxvhiHr1VTVntnOq5Q/36h2rwG/z6LqfiV40fmUZo/7HM8VmSNVQcte2WjRkgWXHJWAtLwfC3GmqvO59Y2fO8TPgSTNcARbyn6wejl3mrfku5MM5W9LBkJWdLaHgfKAW8N8EJrQYFOCcr6+OcBDw/oLIxvOmA5EojtPvlqBIopgXjLpkbruYEiok2OUtISe2z6LwZePdGQkaBaZo4FVuMWvVDRC3YDC7Q+ZkQPEWgK5FpnmsLOFe1ol2NKixh54L1/MS5FWqwvhNuhZGk4X4XBjx6MwpdDGw3AzEGrq/27DZb9tst22GwJFpLTlauckvuNqZOqvBBqbS10/FUDXghF1Pnawbc6ILW7GCaFkouHI8j0zThgyeWTAieru8R39rusdI535yT4hzO2X3UCkM1uU97SV3A+vRxVtgTGvVQxCI1We/v+eGc5SfEQcTyI86ZjDGcPUBx4HJrhNvmYQu1DUawyKEmf9U06btg0Na5bNwOVCvHWrFIYT3HomvStRRlmhfE+ao0KMy1gKo1rEar7nlxFxWx717o65Jxpt9v4m/mtfd9j7iC6ILoxDI5phhs4yyJaTwhwOPjPfMyEboeBYZhsXvlPcGHs0pngzJWDFyqnT6TBp6snkujfnauUTFUQNdVJut9uXjZ+ec7oc0Lr1ufeuCXk2j1+i8+80PGj5vEhFpWbOH2po9sho7NYEktbw3rSOoY1aQtD6NVP3XB0XemChy8ieKbSllmXE68vHtJiB3bLhCDo9/ueP7pF+Y1S0RFeHl35LvXlmz8/uWj4djFM/RbQsiEsDCnxGF8YJxmExlaGQaulvFr9bgz07wwL4kQI5vdznrjiSOlzMPhAEsi1OpIkSpzIULfm9fbkrZWVHMyM5ETpBOOzLAJBBGGPjydgkV5+eqOb+5fGutDWthvqnPeS/WIe7786U/Y+x2cDiRJBCcc7q1S8PmzZ1xdXeOdFSo574zelxJZFR8C/TDw2RefEft+NbSNceKd9Vnc77bstls+/eQ5XYw8u9qxqep2V3tT6RtiRJfE8WQ9QlPKHKfRCnpOJ8ZprqX0czXgJudqYaYVyZhMa9Vc8d7YGrXIZ1kSKWdCDMShJ3aROWf2V3u6GOiHjsfjaU2anuekVINbqXJLojEQLLEaqnGvTX/VEpDOtwXuQALem1LdZrOn7wcrXR8M4ug6i4Yay+WSpnFmXbWchpJrpWkIlldoGuMG1ZZVOzzU5snkBfJC9MK2c0RnrCNKqlGNFfdk1+bwGfbzMRJFmLLy8v4A9we++vZ7cil8//0rUlosZ5KKYd/OUVKmXG3rBvcOAHfdFB3UwrndLjLEK0opfFKuyTqzTAfm6YHxdOTr/S8ZDwdevfyeu9evOJ0euHv9Pc559lfP2Wz27K9ueP7J53T9wO2z5wzDQIyRru/qnJTq+V/Q8+RN0/v2sGbcpcKR5QnwoYBU8bvVm1Z4h2l++zKsBv4M27TK06xKVt6KCH/d+PELeRqGWml7lgCzR6vKUoSMJ+FIyQo5pmw8WRpx3zUMSkklmeiQKn1weHX40DFsdtXztjLv8TQzTgvjtHA6WZFCFDEKWsXMSsWvzbg/TWxQd83mebekk1u91FILShLeeZLkytAIZ69VuFDzM+1oVUiubkilGN9XCjEI0QeToL0YijJOE/cPY8WfjfKV07jCND4Im5KYcyJpYU4LbrKWYMVBcI7r/dWKJzapAm24HFTtE89utyWlXNkY1XjUoqEXz55xc33F9dWeL774nL6L3Ow2DF2kC4E+2pTTkmvR0cI0Tswp8XA8sqTEw+GR4zga06Ma8tPpxFK1uHNj7YRWBWiFXpZIbPfEvHkfA11OxCVyPY3EvrMZ5Z1VK77lgdekU5W1KFpTi8LZu3b+wts22IWmHY9UrzvgXCSEjq4bKmxjGuG+FqRJnQs0PZ2aC7DITmmSe7kWB8UQGIau5k1qrq1kWlGPx35qFjQZvbYLUotQamTWJH+xayVY8VGqDSCaE5GLMs5GvW1VradxrBtGfYg1OSl1nvyapQ4Vq0dMmqLzva0x8RQSaelYpsjYD5weHgnO8fDwmpxNP32ZEiAsS+HY24YeYs8wbAzqqeynBoq0CudVkwVZ7bZe/Ld51ufVdM7LQYNQ1jOpnnKrJn4b235nDlLf2AZaayxt8+yNaO4Dx4+exBSMcz30g1XRdRZeC9gkSZkYIrv9DdPJ8erhe3JOjOqYRMiSQRayy/g+02+EOHT0vVVI+Widd4btnpvbFzgfEB8pCn/6i694/N50L1ZdkjIhatnxJVV2Scmmbx2E7eAYYkHnA2kUlqgsvlDyRBfNcE7TxDhNnMaR4/FoiZppZFkSscEoddF6JxU3LoZd5oRqRtIBSY90OuNjxouyCdYW6y1KlCrLOHJ6vDdPu+sx6ViH73o225799daw7W1HCDCNjzzejQSBnTf63yZY89049EzzVCsIzUMsy8QQHbIb+PmXn/Hp81tar0RXecbee26vr9nvd9XY9DgRpunINJZzuFj03BhhmpnHmSVnHk8nUs48no6cpqkq41nJf8pplUOlGlSXXMXCa9u5unk5EWKt8rOkV2+skK7HhYBSVf/eAaE45+mHziTnFKRUFo+379Xc2/2poqROTP7g7ElbSN/XFnPDZkPX9XhvipC2mVYYTOz62tv8Gt6vjaUrtJGSsVC8vxDCqvNGxa4nRSkpmc738ZF5POAF5mDUvTkKnbfkbR8qx94HnLhaMdyKWaqkqw+42LMUGOcDOSfbvOOGrig+mUHKzqPi8O+kTpzxaFmjiwpBXMAZIs4KtEJfi396Pv9JYRknNpsNz25vOR2PfPfd96TFjPlpPNTrbdBo0cJut+fq6opye0sIkd1uW71/mwtPPOUKT12yulaw5ILd8pa9alBMvVaWu7zgwa9JzDMW/uRTLIxbYcl6p1fo5FeLHb89fnQIpdFnhn5gv9sz9IOJrSvWokqVGDr217fghFl6xpw4aWQkEiQjbkLJuD7Tb4Vu068GPISICx2b7RU3zz8ldh39sKMofPVnL3l4ONaO7lX7ZDmheTYDvsw1K5xxDvpOIHs2QdH5gXTKLEFZnOlzdJ0J96c0czoeOJ5GDo+HmmmfrIM7tnC8GLWvSY1KM+DFupe79IgsD0Rf2Ha5SthawjZ50yluQ4F5PHG4v6MLEdlaB/PdZs9m03P7/JrPvvjEIpxomOvD3QOvv/2GIMIcIl3w9A5cXgh9x+FkwmLdsMEH05seOs/Qea62P6nz0CafD4HNsCEEz267ZRgGlmXhdDqSloW71wfG04llnhlH8+bG01Q9bMtBpJw5ThOpmKc3VqXB3IS5ak7Ee4sCgCpaJAzFFlPwgdiZd9tVFpPznlB56LHr8cFK0efaeuytJKb3DJstXoM18S3WVLqPvuZj5oo5e5wEgo9cXd0QY1chEYNFWgLUoKjKi49uNb7SVBgb/tqqtepzZicsDFhmK0SzANM2r1ZgtWLZFEpeKGlmPDzweG8NR0Zva2yMji4YdZDtxpLWwa5rS56BmD47gI+42KNL4jTN5Jy4udqy2/SkovS1QfiYCkktgnvT9JzZHKzR9jlf1Sh0WrFrJXYDIgMMhf32Ci2ZZ89vOTx8xutXrxCBw+HIq1f3HE9HS4COEzF2pLSw3e755BOb531vHYVC60daD65JZTSP2GQJmojVpXFv/z5XV9qtaZh57RkgF29p57iev56/8y3LVyPc9bVGxPhhAMo/Cx64NOF3q1ZzPqy7U9u1Ywhs+p5l7hGxirUiHUl6RDIJQSXjwkTsTPRmng6EnBi6K7wPpFQ4Ho74eWaaLHQexyOlLJScSEvVmE4zmpdVw6RUbWowVkeQTJBEdJkoCSkzeani/zVEtU7inlghIVVf+a81CVKKycIWtaovqgYJ2QxESbiSiBQ6p/RVSCdILel98yKqebTz6YjGiBdFcyQ+v2a3Gbje73h+a9oW8zJZk1Zh7eFpnl2FrFoMZxQJvFiCU1plork0du/qJDRmRV8Np8mKpryQ8sKSFuZlZlpmlsVyCiUXktaWHM6DNw2LiOBLMQ8wRFpHdOAiweoqdEJNBloSrHH0N5utHUcXCSFWqlmlfFZvtiVHlyXVHpYX8xHzwp36NXFpgkzWKzJU38uJR7DkmffBpGG9X5koqxSBPyeoV4UkuSgmWhkUlwa8HU0r7q51m1XtUABKM+AFNNc5bA0llmVmnme7x9GU+Io3Lvda/FKsYIUWvnOB5yomVNaZaub19TWlZF7cXrHfDcwpc5wSKRfcaWJOuTazfrf3uNLv6km3U24SAAhVgrhJ89omqDU/pGXPsiT2V9eWVH0cETE4Jy0TaGE8HRCUx4fAMET6fiB4Y+b0w0DX9QbhSDOe7YQvvODmoev5WpxB3HqcvKeS911PWpKi/nph2N948QrMtmP7AeNHr8T0PhBipB92bLazUZbUU4qvegmOq93OcFoPoduiY2Lxt4z+miSJIhNCotsFfH/k8Xjg5bd/Quy27Pef0fVbHh8P/NEf/TGKmuJaKXz//Xfk9Mg8jTw+3J9ZCaqkAlNud9J40kEnohzZO+UmnNhGKzCZ0iMZT6ZHizAMAee2hFq9Ny+pNv81w7gsM1oCs49krwRdDOEvEyk/4DTR6YkgM1vvuepNf0OrGH5BrStIHarK6eGO119/RQiB06Znt9vyV//yT/n5Tz7ly599wT//V/8yKS388Z/8Iff39wzBcbUZCE64ilatt+kCXW3jF8Xaom0702cPIRB7C3FNd9tVJUOqtEBtDVY9rNOYeDzcM04Trx7uOZ5O5KykVGEQ58EFfOdMW0KEK2/msSVH68kBrLDD6sE2rFpMU6brehNe2gw1qWl4q3VDMqnReU5MFVNPaWE+WXT0ZE46R3Q9QqQkE1eLwZln7xx97wm+4t+lvj6a4xGi6Z6ArJvdmYEiqDb20IUReeNxDuZ1lUjNZSaXhbwsTKdTxbur84NpoZScmE4PpDTzcH/H3evXxODRTUf0nj44kEhRYc5Vp8NRu9K41t9qNej9MHB7+wxQfvLFpwTn+OKzZzy73nM4jXz/6p5xmvnqm+94PJ7YbYYnnmpd4OvGJa3ICEG8OSyVjUeRJvfbPF4rWEcd8foZ11fX7LbXUByHwyPjKXOoOjin04NFM3mki5GH11/z3Td/Sj8MfPrp5wzDwLMXn3Bz+4wYO3a7Xb0nNp/MkfL1vMu6aZtX/lSrfqUZnlfe5cnWf7/DOF9cGGlFQ820tAjsgrX09oV8//jRDbjUm+wuPXBtJFdBcBXX7ei6zi6+eAqRRAd4gghOEi70OJeQ44FlPgEWgoo45nnh4eGh8k9PNTF2oJSFnGeW5VTDdVtsqWBJUqV6vQURNfVDl/GS8CyGpRZHkc68HC4TsrWsPJdVYKcl2LIYbxxANAMJzQuSJtCE94nolOisnyFYW6k3HMZ15LSwTCdry+aUFC1huN9tuN7vuL25Yp4nuhBrZWEt7nDWgDcG69jjq5aJd6aDYjS0Wn3aGTMlxoh4t6oIGrXQyriNpw2IsSdyTqRiGGspFnYbhS5UZkJbTH7dBErKF3g31ctvnizrJuGq19bVudEigaZ+J0JNzmE9HXNZNXFSWlb99idzktaKzp9pnI1aWWmhMfgqR2qKmWvSsSXpWpTS4AK5XMQV/3x7rQMNh22FX7l6zKbznUsipRlqZxjBNlsvWFeclFZGT0oJh1JKrMJMrZBGWn1UzZGeKz/PJhS8C8Suwzuh7yyafHZ7y/PbPfHxZLr0ceLu4ZGUci02emN913v3tDKxXYEL4SeorIz6nhUjb3rqkZwy+/0VIo6uFtJZbmCBDPMEJc2UspDSRD8N9F1knrd0NULTkun7Du/VqpsbrqIXpZg0T/xtg3xG8y+eOQcRb9/PN+z7BUL2ntv/Hu/+V4wfXY0wdpGu6xg2Vl4dQkC9JztHFk+ymmYCSux6K2GfJpYlc3f/yG7ouHl2RfBKXgqaA3HIxO4R5zvGccTd3/H48IBzpqFhuhImzp/zYgUFo8EgoRtw3kq0p8WMSBcc3nn67Z6rMBCc8v3rR16+fuDlw8j9cWbYXfPs059VjNV66E3jyOtXL5nnhcfHY+2+XrPwPlCyFXZsOyGY3j1BDB8fukAfldja3mNaJ0XVKuDeuNM5Z9JimtjX19fcPrvhpz/7GX/pn/tLvHjxnN1mRx97fvL5l1ztr8nLTJpnvBN2oSM4x363ZTMM1kyg7ysevCPG3qpdawGRq73B8jIbm6V6000aV4HQDdw8/4Ttkug2N0yzdZOZ5qXed/t8827MM/MXyabmDmr9WdbCirzi4pcrZE4ZXRKH07EiQGb0SsorfzwtxjRq9jTP47qJtlFUWXImkXDURLivPSuBaVqYJ8Pvl2UBldW73F9ds9vvcb5GK1KbFlw4KtAMBFZfUIt2smZM0zvXY8+UXI11tkbKZTGoD5S4GrhQuyw5vGzIOa5qmsF79pue4B277cDQd6tRLeJI6kzLutQaiIvr6bxjM5jsbBcc3gvzNHJ3l5mmBbQQnHB7dcXQD+gFu+XJGrevWzfbpr7nhLUBm1YsuBlvRFrPC9u4svHT9/trvI/c3j7j/u6eh/t7jo8PtYmJUCQzz4mcJqbpQEoTIXTc3b3k66+vGYYNt8+eE2PHfn9F329M178z5UpXmVSuJhlbjYdSkap6LivfvB7v0yQlayT15C+XFnu91OdNS3gqxPWh40M68vwc+LeAL7DL+geq+r8WkefAvw38HvDHwL+kqq9+wHfjnFTPz3Qi+o0t7iaQlMQjONRHggih6+m73hoGp8zDfCQ4Rxx29NGxzAt5EWI/E7odIp5pmlF9NPx1XqpHY55Xc5JSWpirQL2Pg2HsCnMy2lLDM7uh53rvSfPI65ffMI0jv/z2Fd++uufZi8+Imxv6fosPEXGeaZq4e31nCb1xrpl+mxzeO0pJhODoXA8xGs7tvHVv2TiGTvBOwRW0adeprhh0G9Z5xQy4E2F/tef29paf/OQLfud3foftdsN2syXnzGeffs7N9S2NR+xFGHzEi9D3XeUZt05JDu9jZViwNqJVmrGxYg8frF+gkzNC72PP1c1zSils9tZBZppmjqcJcY5hs1kLRlrnHqkQQ+sYfpYhODMxlmQbbmMKqKr1yUwLKVlTjVQ97JxMPyXPBoG1IpQQPLHzaN3I37yWKWcWnEnv+rh6/6gyzSMlmxEfp7EKWUmFlITQRWJUYt+d+ceuLUvb+FqBTi5muIsW5mRa8iklUl7Qksh5NMtRMqLFJBKScbh98FX72tN1AVTxlXYqaM2/eLa9VdluehP4KsVU/wASDtEqBZEvORC6ipCZATfGy7LM5OVkLQyL5VCur/bsinIaTxyPxycRzROVPZqB0qcGXM4G3L5aViPeEqC2Vh3b3Z4QItdX11xfX5OWeeW1Gw3PscyFufbNfDzcI+J4+eo7+n7DZrPlk08+pe8HXrz4lKurK4Zhy9XVDd4HhmHAEWllroqwYBpMq9EV1o2YuhpWOP0drJNzlx59YsTfQpvOM+Qdf33/+BAPPAH/C1X9f4rIFfAfi8j/FfifAv+Bqv4dEflbwN8C/rUP/maANUFjFKlQOcJFTT3dsustw2tYa9d3DENPOs3Vo1JSxrjREnF+wMcdw+bKCjuyMo0T0zwxjhPOOePShiZlWqzEvGpzD8NA7LfMOsEprRzNUhQVhwsRVwo+9PhOGbZ79lnohw0pFUQm8mmiKBwPx5WitVRc1zmrHrSE2ExQRy/CJkCUwsYrQZQ+FMzhLUjl95q0P6Snez4iUtXu9lzdPOOzz3/Cixcv6IYtBceSlOPJaHtLglxMo7lJeyYJJvFZHCVVL6OShJ1LtAa01UWi1O6EpzFxGhPeQ2bGuaZsaA9j9ijznGs/0cwy2xfkbPdzXhJzap6sLYwYDMrRC6PbusIvy8w4TebxVRVA4wmb0NM4jWuyr5QqdNTEtNusqx6b5repYkLDlk3neymQkjJPNleW5USp3ZpytuYe1hfUsG6DkKoscpWYdS1RqKkapHL2tqsBT3lZjZH1+NJq4IweaRocVUcFg00cTZdDa2RUe6CGSOl6g8IqZGaNU1rbu5aoa8UochHX1zRd3ThzKcxzq+xdQK2YLZe2IZ2x7Xet7ydwSetN2bjVUmgty6xzjb51P+xjtOYTqpdclS3dkyKo8y1ulbbU9W0J3plJhIf7O8Z4QlDG04Fh2HA6PtYE+L7mMyIhWLVnKXYG1p/U11qNcJGUbDzwsykXLP1s1+yc11gtvcIT+opcHv2bv//q8SEt1b4Cvqq/P4jI3wd+CvxN4G/Ul/2bwH/IDzTgqoqWBTQQO89m15OzsiQ7gUQtNqhxWAiBm+trvBNepVccDyPTkhmXgoqnC3t82LDZd9xqYJkX7l89MI8nHg8HHh8PbLcbfv7zLxmGwarKciJKtAYA3rG9fkG32ZFf3vPdve3wKVcqkAR8twUX6fcLfpn5pLth/9y0FsZx5ngcubt/4HSamObF6IMpM46mTjgMjq7riS4zcCBSuA4bnvfDKo3rRSvOXhd4SefpIUIin3d9zODsr2/55Iuf8Tu/+7v8N//6f5dnz55x9ewz5uKZT4mHwwOtBLpgmtreRwRICKLCcW5pm7yGww0OklVKTVESivUHnaYJkQUfEorweDxyqnDUkkzDuywZzda8Ofhgn5DNcN0/PHL/8GAshAqhbDYbuqE3DZRkkMI4WtJqmoxbr431c97madEBsMraBucYfDRvtXVAyspSCprfgYGL0TtVHcs0167oM9N4WuVcVcta8RdCZLvZWPPtGMgl47IjZ7tnqoXiHKnktRS9FSS1eyuq1XDqit07p2s3peBDDe0h1kggLyYv671pWTsRyyFA/T2srdycGJPI1QpOqMViWqzwyU68essNg1dytghmnu3c0zwa5iwOJ8a82e+viV3zWp8OW7aWZLVunxY1iZrThhit9tI5uMSV6wQ835eqMtl1VrwTu8jawebNxJ+2eYEd9zIzHR95vH+FiPBnVcu/6y3hH2Lk6uqWvhvYbPfsdjdGBw0DTjzDsKPvt8Suw/WdSUE06qc2akGjKdq5FHTdCGmnt+5Xdt9WwbY6n5sGy4eOH4SBi8jvAf8t4P8BfF6NO6r6lYh89p73/D7w+wA3Nzdv/FXXTjBOFO+lRk8XCZW6QwkgTlYdjtY5vuF3uVBVA00v3NgsDpFDnYyZeZ5Nf0GsiCbX/oVOHCH6lTrVXyghmgdeLgqy3Jp0dap0RAhUiGBhqZzv4/Fkkq01eWcPKv7t8U4JTolS6Jw9ei8rC8SAJMMVm1GyOf423xYRun5gu79id3XF1fUtu6trnI8s2dQJy9KE+GvI6pTS8mx2E1mLFy7g0CZ5KxYDg5wN+DIl5jmDFGofDA6HicNprHKrloTTxbxl0wO3Cb1UPPpwOPL4eECRlYKXi9LnqoNSaX+n00haFsZp5HQ41s2/eoYCT6VLBY2BWEwDp+DBga8nfFmw8S6Hr3mC52TnwjxPFW6xR6gwmav9UW1O+jarDcoQqclG+6ym751qazJaFSUXmkpq0KKjFe2cm/J6kUpLVTQbw+nS+2yQhfPedIBgZcBcJlGlGUsVWsuwdtxa50Be5Z1TVVnMVhWbZkS8Ccf5QC5l7QH7zvV/sdbPKdJmqHV9wVlz5O253f7Qin/WU9HmBNZIrUVARddkqRnVjLPWGZSllq4vS40AJ3KZiSGiCn03WH1AFRoLca6CY1RbkOn6jhKCRbGNz79exDMU1ZysJ87W+dCfPH9xmX7Q+GADLiJ74P8E/M9V9f5d2rrvGqr6B8AfAHz55ZdPDq/kxHx6xGmCAtHbjpSkGiyt3FI1XM+LsBkGKIW72IE4coHDuLBkOPlSqUkO5/eEPnP7IpCvTb/h5au7qg5oBn9eLCHT9x3b/Y6u67i9ecbu6polCa/uRytRv3vFOM8cBsdha9WWSMAHRxcCHs/pNHL3ODHNicfTzONxrNl9MbvoIi7CsDGYo5eZWwe9W3i26XnWR4KHwdXi3MKq763Fr5uVXhjXNpxzfPmzn7G9/SmffPYZ189e0G0G7h9nHk+vrUtPMgOe60IRb4nHFXO7cIJWg865NP0M0NVwF8PdW1m7qi2ex8Mjp/FI07FeMShVutgxdAONyplL4XA4cXicqW4nIsI4FnyYzOvL1nl+mWdjWSyLNWTmvKC9l7WE2le2j1ePFMMyM2XF772ampx1C+ICo7SRS2FcTszZ8/jwaK3RqsHx3nF1dW2882Fgs93V0NtolpdGaZrGNv/tfLUw58UiqXbcoeqzi0UMqxyvbwac1diuZrpez+a4iFShs4Y1S9tw6zuUC2ZQS6b6+lkWaRkcZJDTNI5WaDWemMaRlE1Myp4bmZcZXxlDMXa4bkuRYFWtb6/+Cn9o7WPJCpU0aIXqraq8adCasbaEfZtPrXn4w8M9Dw8PPDzck1Jimqzmwt6pNG36poWP9/WaXjhAqtYsI9v8e/3wUJ2ziHctqjDoabu9Ytjs2G63fPbZp3Rdx9XVlmFjUVjfG/spxN7gNO8JLvCkckPrplLtgh2s0PB2q+/9YeODDLiIRMx4/+9V9f9cn/5aRH5Sve+fAN/8wO+2xNs0kkQRDdZpxWUcmeaDC9IE2HACfRcpuV/1SrIq02z63SLWdDYGq+x0XtmHiGjm5ctXJpJfmrduEo4mPtURY09fq0Gv9lc8Hmf22y0CvK6Tdxo7pnFeDYZznuis2/20KEsqTHM2bZVpwYSN6ibkAoIQ+w2b7Z7BzVwx0jvjee+ix4t55GA89Fy767hqHC1p9zZM6MTx4pNn3H5uXYI2V1a8dDhMzPNUMWnzhq2ApvlDBo+sGh800c2zN7Emki6XlpwNC7TmA+a9HQ6PjFODG+bV3gvQxYXNUM+vetbjaWY6VS52U3ocs2lMl0zKlrAs81Kx62zJvIb5torEGtKuwvgqVXTIqhS1Yb81nPPhXEzzJGKvm8WUhNPpyOl4rIVB3ZpIu7q6oh82bLc7q1btLCK0xOlSJQDmWjBk55m0MJMQsfZ3ofLau1i7FtVuQcE7q2oUObM0NNdcQKkMlZqTUbXNWQrqzl3YLSpx9f7VtUbz/tr1alFIqRISlkBtmjPH05HT6VSriMdVC2We55oz6um6xNXNbOqAb+QZoO35baa96X1eeuNP36P1xqzOhXikJupzyqa8eTpxPB0t0k0LKVkjkbX7vLcErfceFxxe1CL4xgTCmE15ycxVpnhOyaL5XEjJrl3D+rfba4bNjqurPYfTa4Zh4NNPb7m+2pnd2JuO/kb2SOxwdJW5JECtbm1JTlWL6FkX0np9Lq/Th4wPYaEI8L8F/r6q/q8u/vTvAf8K8Hfqz3/3B3wvYNS34/FoWsrdFh8cWcBJqTmOGgq51qjUdlbvTfSptelqsMqw6eliYFlGTqdHq1rLJ8iJccm42IEPnOYFdZNhwd2GfrNnu7+1BGY3WGY/duy2GwA2w5ZSoZCHh8dqK2yGLepZ1HM8jbx+PDHPM2NSFnXWDUZz3dU7nA8s6jjNGbyy9AHvYfaB2XscNfrQwkzVEq87s6IsapzmpU2IOhS1BZcz02zl6M55pimTFmOuaKNDSVs6tTKQVmBy7gayQorSzHpdUIK97oIaty7FAqqCq/i6VctemkeDM3L1uhoYQQ35DS6zl1u/y2IMjYpTl5QtHGixbFWflLoRg+Jq1OKAUtUR1Qk++prs8xUys40ehUZIXOe7M4qjesduX6w4x5t+uPdWji+1kUfKueK5FbapuHarIL3UM/dOGIIxU2IXz8a66pI0j9mcjAxqn28MnKX2qGyaPZl5Hsk5MfQ92+3WJAbEPMzLybEaTbXoy87YIIfjdGKpDJ5pnsgpPzHa4zSRU1p1aVZBNzFxNwVevXrFaRwZomPT+TeMj9bZed7+zWFw1Xmoprw18mgbMRi80+YNVml8f3/P6XhkPB5I8wQlE70HNcOel7TaBlUhB3u/k2LG3CkuND78GZZ1ahtE8IJzdn62adaO9Wq1I6XWa3z/vZEe0vLI3aavNGjrEbDbX9PF2meg3yLOE6MVlwUfCNXxtIpyqU5LgwHPa/JDx4d44P894H8C/H9F5P9dn/vXMcP974jIvwr8CfAvfvC31rHMM69evqLvO26eezZdh7pCktr1Lpshd8F0J0w61lT+us7YKMMwGAWx7/n000+4utrz9Z99xddff80yTSzjPTnNjONM3OzBe+4OI4cpc7W/Yru/YXdzy/PPfsrQ92x68462my3Pb58x9APHxwPBe/L0wNfffFdZBOYBPUyF41yYU+E41TB0KeQSVt6zSWB2xNAzZser48LcFfabDmLgFAJd8GYMKh/4VJSlGEcaFyhAwuhSXiPNmQTzGl+/fsW3d7NNlH/8xzWUDqBn/WkzloFW8SUXHgK0Cke3hvctcdRK2I1pIWsWXnywPoXVcKPg6Ri8R2iJKpMLKFXEKNVFnVsk4T2+68xg19L2nNOa5MzZqJ+SW2LVdhITcTITYRCARShOq5frrRCpi4EoHVLVLa01l+UZUCjyxN7hvWcTd0Qim92eJrzkq85J13W4YPdjmmdAmWhrsXaJquG+4bNm0GPs2Oy2azFQYyKt5eSND17VHy06nSi5MJ6OVqswTxxPh8rGMU2Z29sbPmlefYMHztDyCiG3Ti8tr5By4u7hjnE6raJrxlIyyGqeTfrAqh2td6Wr3rvllApME4+Pj6gqX3z6gt/58rOnHGkUS1TWCsdicCDFokCp8Elpc7kmshu2fYklj8eRr7/6JYfHR+5ffc90fEDTzKYLeCncPRyY58W0jvreqrZ9oXjbWLPLq+KjSSNINaTWYxWgClqyLAtOqpxGsoTz4TCRS+HxIXB4/N6ikN4KnJz3xGDSDbe3nzAMG3a7K/b7a7rYcXV9S+x69ttrdpsdPkRit7GcSZAzIV7C2bB/4PgQFsr/nfdvCf/CB3/TO0bjESvCXvW8+wjnZIAq50qpuqOrrgYUqMI8ns1mw36/53G3Y7PZ4kRIy9HaR4WObjAmhI+9lT73A/1mSzdsid2GEDtrEOCM+x1jR0yZ2PXEzqo1kwqlsDYcGKfMcSqkoixZa1bfdEPwYv1MfUBCh4QOlUDGkRASkQUY1dMVV8+3fq4W5mLhozbWQPGUAj2e/kl6CFvch+MTr1mkQ2iT1tcEVzXgLjwJ8aj4aRNact5+xnDmQbcF4KtRFx/tgeAwiIiS15yFiHl72SkqRq9r5cq5JjhzSpSqj5KTGXBjB1khTiqGo0vJhns1/EasCay5UsqZiVLWMnHU8ZTEC62FlYiuuPibw3jb507mqwGvlL526YuWGvJr7cnozmHE5c1pn2tXmQYFny3thcBSZSIY+8O47ONozSnmeaodhzIpzTUxb/x+X5P6jcXQjsmqLQ1qaZvjUtlX0zIzLdZ+bl5qsVOFtpaUrKtUTrW4qIlOVT2VC3qnMWuu3rqSbY1qNchr0ljPVcmrE1Jv7RN4UBvU0bRrLKEMrJW72+0GPwfGKdXEY80X1YeUYhFsywuIRVktslIRsjvbo5ZrEkk1J+HrHctItsi71Dk+kUiLfedc7cVmM1Z1zt42SufIKZmsQ16sCE0coeS6iTV2fIMqf9j4USsxxznx1csDXVzYvoB96I3Bxmw30gjYxklW8zSn2XjAp9OR0+mRvo9shsjV1Zbf+92f8tMvv+RnP/2M3/mdLzk8PvIP/uE/4PXrV2t22nvPZrMl+MD19Q37/d70hG+fWek7CUchbjzbK490E1fHhPR75mHD1A+M08j9d98xzTP3IxxmKHiyGIUkDL31fKxeroXlm2oAPeocc3R8L56o8HoUYqqhPQCFabZFVlRIajrR2SJrvuiv+ZKL8LgUvvv2a/7RH/7SSqqXBVVql3LTnA6hq15463Rk7ZKfyFr6ZqTOHnisjR2MAujr5G/9CAdc6BHxBNfbRrFCMXr2LgOmanCxAedknqmVflfPu2qWGDe6hd527101lpcGXIMHJ0Qvq+51CJbI7Dx0UaqGuuKcEiTjUTu+UjebN4ZiWvFZWDcuJ27VsUbOeLLDkm8tcbrqdCNIsA7KiUxOoDmzjDPOCWkt8KlVrVxEzhVymU4nXn77LfM0cX93x/FwqJ3sW/GKbXDBe66v9pS+sB2GlV3VWsDlCnvM00SqssnjeCKXzGk6sVRpgeUJxdHW2TxNq0FrMJFzzgrTjlbjcHi0Irnbq+3qULUr2cr/XanHQ7FWns1ZqXCaVtRHi2IvaMAclJTIaWGaZpY5kVNhv7vCi6D6Gb/z899lmRd++fW3PBwO3N/f8+ruNZpNz9xJ28QL282G/bWV1X/+xRfc3t5Y4tx7cik8PD4wTTMvX37POFoHqxf7q1XD34dgMNY0rQnfJS2IGMtNRBmGDddXN1zf3PDs9vmZN+4qaWM64ZM1pnbO46RDxJPyvNaM/JCmDj+qAU+5cBwTsYMlC7gIciEupM0Dp3ontvCtGepSE0WJED193/Hs2Q1ffPEJ19c7rq+vuH944OX9PXldHbIq1rWKrl2t7uo2WwtpS1UDjBAHJUtg2O5r2XGGkkjqmIvjtChjgjFXqMNbxjrGDaFKl8Zus2a2EVd3WSF7x0F6PMJjUiRZpr71K56r7nFWmEvDMG23viodl6poqsrh8Mj3331DWhaW0Wh2wXdV2Kkz7FYuvG01I94SmBa+iiUCneACZwNe1RVtIlb9a+9xYYOLG5wLRL9ZO4c7GjxQPzoKXDAqqAa8VQWuvzfudENN6wla/6O6qCsUJE4g1642sQpieTOcRr2jNnSuVX9O101FKj3zfZrPqw8tTR7W1PHk6V9ZUSxhZZZA/d1XHZ5cfXQ1ve5W/wJmvNd8gm+Rpx3TvMw8Pj6aHMPr1xweH6sn2rjrZujGcWRZltqRqHmQjQJYOxrlzHiqXvwyczjWzSAZPdA8Y8Prl/q+eVmYUo1+1vO0Y20GPi0Lh8PB4J1peutats9tOQGRUm2pVGjH7nFNadT3lzUIhwaPpZWTrlqq0qBpv3ddZw2scfT3D6RSeHl3V4ujCk7qZlcKsesQb71wb26f8elnBvm4GKx7U4gcTycOh6NFXd6z2+5WRcO+75mmiYf7e4sG5gUtyxpplGLS18MwsN1s2W639XrV+VJMRE1VccnjfMYXsQRtSWv/3B/ihv+4BrwI94sn4DnkwKjRcF7XoZprIs0EplCrSURMbMiaE2y4ud7z+acvuL29Ybfp8E7pO8/Vfotzwpc/+zn9/pq7uwdevb6jFCGNCecKiRPHGgJ5fzQwQRecZk5z4uFkfSP/7NWRw/FEGmfSWJgWR45XoD0+Cl2uyTxn8ITveggRfERjD+IoElYDXtTaWWWNSLEema4mRkNFi2YJJniFdecxveDqlcgbwkECMXo2Q2RxhbI0jrRpaLjOE/uKb9eC5pIKJS2VQVIxynWlNj411QM99/hsm5F159ni4gaRgA8bEI+jqc+dPVK/tj47J9lyNq+75MosoZVCVPhDmsdnHq13Z3aBdRyyptY+eLZDZDt0eAdD56okAXhRfKtkvUjSBu/poqe42vnock6mxMPxnkUD+6s9wzDYe1tmt96DNW6phkdR5qU2Pi7Zmivomf64wlO0SAS6vmOzNZ2Zrjf4TtdsLqv3H0Og62rXd+nqkdo9il1cPfOHhwdOIbDMlpgsuaxsmGmarUw/JZP0LaU6CXk1+JYcNQ38ZZ6Zl8XuX3UWTktmBqZxZDydrCtTxdbfZ3MuGUwXaZu6QV5sgFCbHJ2lW+0aWKXrZrvl08+/WCVzSzZMO4bAkhJ+2PF4OHL97Dm76xvTcW/XuzK4tpsNn376CcMw8NnnP+X5ixeIN0gwl0zst0zjROg2hG6Dc479bmdqqVUQK6Vk556zbbCNLorQxY7Pf/Jzrq+u2O2v2O2vWdkAQJCIl2AQbQjVYXpalv9uUO/940c14HMRXk0eKZH71HHU3k7CWTNekylLJnakbmVLiHMMQ8/11Y4Xz5/xuz//Cbe3N1xfmdCUHyLD0LPd7Zhcx6enkX/4j/6Yb+9HK+eerMvLw6jEMNEa6aoqoRbQTKlwWGySf/P9vbWTyiOkTCmepb+FCFECjmCTD+z4xJom4wMaupp0M7w5q1HAijgSli0PeOvY7oToAFUmWcgkipiolxqGhFBIcvbA6jfSd5H9fmA6ZtJJyRgjAlW8C/Qbw8F99cDn08KcJlRt0ZZc1rJu85oqh/tND1UMl0fMgPu4Awngq4YMzsryq5csIlUB0bLwXYzAGZNdV79gCR1HqxOvDa7dWlnonaOLkeCsy8w+eroYuNpuuN5v8A66aIeY00TJCScFR8JpoxgazXQzRNIiTG8kjNKy8PrV94zZ+Nh9b+JkDUFpl+QMPNWFV2GKaTpZmD2Oqz5NTo1vbfesFEuCX1/veaa3xqmumxzVixal6uT7qsudad181o1NYBh687Br4lFg7fZuWLbN9VyasT17i3M16rl2PjKlzsq/T+aJm1NhydFxmigVh7akZ8XML+iKT8ZFPuBcPHU24oLQGBj2RINqzh/hgqsR4DW77b5+bv1bze3kkvnkJ3eM88h333/Hl19/jZaycqpb3qGLHfv9nq6LfPKJaaE0D1y18Ow0sqTEs0++4MWnXyKYnrjzzsgSnbWA09oj4Hg4MM1TTQ4bPHtzfUvf91Yt2m9sDTQpBKTq/ZTKaFGyt1yOVmaMyg8x3z+yAc8qTNm86sOUeTjOuJIIxaAwz7njeGNSBB8IIdP3vYk0bQc2m56hdt9oIW4pkEtmnGeOp5njaN07UrLkkBZlWgrep9WAo4pXI+3NxeCROSXGOTOlYgysWpmYCRQHKpEikct9U8V8FhXz/wRrfosYlq3thqmsP4sKUppZFoxG5Sp4YOdlobPjbfJbgxma19yi0arid5G4C8FgDikRKRmfTPMju0rHK3WCtmYCNelYz8yOTQwFFok4tyD+3KA50ZJTukrfmhSB6X+s/OPq9TdURZzBOmjlcHPulWpFGbYBDF1kM/R0wYSa+ugZgic6MXhEizXHqJudzZ2KvNTkrK+YPrVa8nIoWkN2V7nsZsS0asO0BPr5qrdNSI2OV6l3UzXgJVuC9pxgrNcUKk59Tjw2ORC0Co01OeJKlZXKcgHWCKkUa+Dc6J/tOIw9YZIFra9pKaXmgiyZvCxVgjafC3lKrj1QW0JPm9jYOeF8bopR3m24L65lg3Qa/ZAKc0tlJq1iIXL2QM9zbV3Ob7ilLZI5P0wjJVpF8na3boJ1NSFIjWQG01wST2nNiVMrrnGAx/uevt8BEGJlYfkO52oU5MCpEnsFF+z219aCzndAoKhjyWrn6ioMpTVvUtcVUkmdnlqrUespfoAP/uN64Bm+G03x6z//5SvuFuWq93yy64gOdhKIDVf1Dt85C2vTwH7TkV7c8pMvPuezz16w323p+wBYefdhXLh7PPH/+U/+Ib/89hX/5Jdf8ye/+KrighVH1JZVPy+ctTpNAslZ78wpFbI2vmg0DJuKZRMpEirO2ryLZoDFeitihtoQoHPzWqFO5updlxVGACmKr11XmrRlLuahtQKV87Dj1jzjyQydp2RlTplUFHGZohPB9dzcbtn0A2VeyJP1/Hw8WN/O03hinM0ALaMt0LTklQu7Mhx0AYRApHM9w9Dz7LNnhNhzWBJjSiw5caqqgbkksiZScUzLfMZUlar3XKMPb1olfQzE6KwXau30s7/a0/cd+83As6sdnXNc94HOOVStJD2XzDwaHa6V/bsKP7jg6QfTPB9qw4tlnrnz7umVLNaVKSdI84ll7NHgkWIwSFtcJi5V505NKN7dveb1q1eWdDudVuPfOL6uqjq2VnSpYv+aLVEvrdGCQhDPdtjQBZtbXYyGB9fO8qnS23LONYw3LoNgQkraGhdjm86SDWO1qkXjdU/HE3lJF3PWeo82PLx1mZorHJTnGa0VtI2F8h4La/M1W1RQZMaNI86rbc4qOAqpOiJFKnhWIwP7vRIX2sbXpjnUmgZbQXYPlDktpFKQELl+9qK+WNaN0apUXdWGcRyOmdN4MDizrv+WOC8l0g3P1/uMwJKwhurCSpV0PtL7Fn3Ydy1JmRfQY6JoWo8bxFQ2q0PhfQ1FOgdBSNPCclwsUZufXstfNX5kDxxOSZhVefU44eMjadez7Ty9d3S+NW81XNU5TwxdrVwT0I6rqx3bzWAtvWoImktimmeOpxPfvXzFL7/+jm9fvub1/WPVzc6rNsk5ypP6ezXgLqJhMAH82iWjksvs2HH1b4GCt6hfYC2bBVql4yXTw1UMH6giRvX7m6jNOkvrpNO1T0l9yZvGu35X5ZALpW4kDleyfccaslnfzmHTQXBo9KTFmiin5LCOP5kkii6mVFgyNfS3z1mPD0HIeDHVxP3WihdkmpHF4RZhUVOL1NQWpm1U0KRFWbFyY16eGwh3vvVwDMQQ2G+sfP1mt+HZ1Y7oHFdBiE6Yl4l5NtitVMMmNUHo0MrPrho0NRkbY0Df4YFbiJyrimuyPpMoZRUrsdfg3Cp/25JrRvM7mgd+MojB14IiqcqAl5TE0jyHlhi5+ClKbZKgxBgrTi2madNao1VD2poKrZj86nrK2ighl0IqmaUWA+VUtU2WtOY47FxqAVL9fK2UQtvYkunqXEAxb4qBXY6GrUvKpCXhipWzO5UqjmxRb2nxnrJ69e/7DrtELSkqNLwlaZNaFmJXuyK1ddQMef2fqjAvdp65mHiewvp5FvnEJ9/drvVlR6i1OfVqP9Son9nkk1NqMhPnTdmLN4ZxRVlFPWQhz4U0Z5alQosfOH5UA64IizjGInx7f+Awz7zeBA6PD2xi4Ge3e/ZDZNsHdt6RUuL+8Z68zNzsB662PbvBWkZ55yjqyEV4eTfyh7/4hpd3B/7J13d88/0j94eFJXuKmkFWp+vNX3ENC2xtATlPE3BofpdBJ1Xmtk0urIy5FKuYXA3yOupn1NXVGB2CVZxevKr+tN/Wz9ezboILnRk8P3MJoyiwTBOnx0dKWkiLiUktxTxwHUejxs0zh2GDTjNRIagYTpxmXLamcAHjR9vkMqPdjPcl7m4w1UJKJ7zf8fknN+yvbgjdgI8d4zRx93hPyqY306oKW0a+ldc759ZJ3dUGz9su0gdPFwK7So3rBlOP807xkykrPuQZSiZrIavBM+JMFM3HgAue2EdiH/HB0w89XR8Nz+x7VHU1XJf3wYSjFErdEFBKbiXocj7++rurSeYQAn3fkb1DsIUYo5V4i/O4ps1R+3sOQ79S9+7v7nFi2vRpscjoeDpWRshcKx8N+mj6Ja3IqFUFN5ZP6+NaSmFqvO7a59Wiqtoi0AeC+NWL0Qq5NO+7QS1aDfkZL6mzteY6mgF7c3Xf3b1mnh7Bb6B7AImrEaNkpDI4WmenFZaByrVu3Gh9YsxbLUFrkqFAahHDOsdkdXZchWB9cwDXjlC+1nS0U6j3ttFG19XFxSZy3uyc96tz2WoAVkJAuYwm7GOcSD0WXXMYdIIEh6aRMh4oZXqrzd+vGj+qAS8iLOIZVfiz1wfQzL4Tvhs8+6HD8wUvrnZkEWIfmJfE/d0daT5xu/uM6/0Nu01PDB7vPFMWUhG+uxv5z/7wz/j+7pE/+eoV3756MN3p4ln5bNWgNoOppWW/DRw5g4qcQzk4G/L6b5PHNAw74S+JChdMjHNCLwRHjN5ydTXsXXN5sopurlrXa1NlEXxVYRRfgOl8IVVZxhPHh3tjQNTqxQXTMUw5MU4TS+x4CJHy/2/vXGNty7K6/htzrsfe+5z76K6qprtpIhAJ8ogCIQYfMQRIBCS0MdFgJOlEEr6YiMZEmvDB+A2jMfoBNQSUjhKQIEiHBANpJcREUVBDWqGhBYSmi3rd13nsvddacw4/jDHnWvvcc6tut9331ol7VO179nOt+RxzPP9ju2UdW9ZeOFjGkZATjWYaT3AJB4kuhXlX1QCAlAfGCWJMvPulu7z04rt410vv5h1338Hldsv9+/cYx5GL7dacailbrctqrqJqJEG8DqfASdOYXTs2rPvOkidcF97uLjm7PGeaBh6dP7ISY40Z4WPbsLp1Qmwibd8Qu9YKLHQdTRtZrVf0q55+bdm7Zpc9ZDwiFtfdBOxwmAZjxtHS8il1P9XmrjIxLN2+7ztyE2m8XFfXWiGF4iyT4ABoMXosszKOA9szc7rudzv2HuWwHXaViWbNNdMTwZEK5wzRRbADWb0oxThydmmZm4MnR0kZb4R109JKdIyRiYlkNvCUKnRECQN8DLmxLnR5nHdj++XBg/u8ur8gactWX0a1oQutgTzlZAXEdU66yVrA0bQeUOVaZopLhwzczW+qME6lvVQGXguEhMa195bN2jBL+n5F03Tkim1el6Tz8cLM57+yYPD2t6nM3pAopZrHisa92KIzai6YPwCcgQNpj4znCBMiI3LNmF5Hz70mZmwaokoNu5oy7MeJGITz7Z4uBtoI6y4wjZPjWVtGYdt2XovRBnQ/TGzHxKPzC+49POPho0v240TF7feNV2vh+YDXELpqmS6RyMU+Ptv4yiQjrpr5L6jS2TIeOFQPdDmtS1p6BepCio/R7iemkmkwrh4CBM+mbLrOs04dvW8h6odojr6UMjkV7cD/+mFUoiOigsSJ0EzVmWUlxwZz4Hklm4LnMWfQXVHtCpLdNLLfXrC7PEeGHauULPEmBKYmsm4bRlF31IQDk1HNTEQrvHJHps1K0GTFfAWvXKPshh3D7sKTnCYymRgcsKgJFrXQBKSx4gchelX6ReX4kpU6z9nBqjxUzOqElz77wZ1zNU9Qv6dVSi8VfGyYzPSUJ1f7k1WeigKjf54GS5sfhz3jNHgN0TmhqTJoE/19br0dtRV2n2kyWOOpzGOyLFb7uTNlZ8DFoVbMJfngUSPyrR8C6iAiWgDQ3FxzLTqpLv7kWZLO2dHkchk775w65r3/tSa6s5bs/iDPoq0SuGnEMfiBqgaPAFQGfpCM5Wa8Eu6pbmqZtep5P4v32QISZu245GCITIgEcmXgzOZRFmurLCFPTFzYdqyxUZC8I6QLRBJtm24GA4+xMY/xFNjuhHHcM+rIo+3Afkz83itvcP9By8U7T0nTbcgjUwKhoVudcOv2O1if3CI0HVkDr92/z+sPzvjN3/59Pvqx3+JiO/Jo58h+GKZIYdqVBI+8SJU/mWStjiBc9qHOdmhMiilxnBWEJpiEX8CJzF5m4WF93y8cdsbAgy6iSVSgwt5LtYW2XcO6t1hhkxoim5wQfTR3QYT1quP2HYtjfTQOwBxRYKeN2eQepntchGASeHSvuks4gyaGnJhyZp8sOie5/bOMzCxXCIa0l7g8f8Anf/fj7B68zntRTiXQqLIiQxD2XUtqo6MJuo03F9zp5JVpYNDk6JJWa3OYRh5uL5ly4nLaM6bESGYgQxTCqkPaSLNu6DdrQtPQbjpj0m5CiX1Lt16Z3bvvaRzLxIpHHJpP5vE0NRfFDsNYJHVLVzemmcmjJ3EUh9400oZgmZrOEDUlhjSheSJlLxDiuOAuLtpNff3J3AiKtijuC0IwcEuUlADNjFn9wLZar+bPMBPMMuXdYpncdp4SiJB1IgWb49F/M47zb6vZoJhmsFqaZS+IFA2KKoEuKYRIGztEW5J0KBbGGtQFm6bgp7s/Sd3+K/Nt5xjyEoapV+5jzNAKP5cNPAtoZhYv0WxzLkQQyw8IqJUsVJP6yQVjXQ7uQJ4dwrkUIy/wCiKL69t9lxI75XfjSJ5G228OwDZ5GGGQPQ0XxCjcunWLrut4GnruEnjT+KTGgKRAnsTCbzRxsd1Dmjhft1zuB4Imc+54QQWTSFus/IGw2w2cXWx5dHHBw7MLtvuJQTsPDzL1129cJe9Cs4w9v4aZedvzefEs0fhmZ0rJEgzzpC6AoEwKn6UVpUgNHk6IXwMBl8ANAc9wG7q+s3T/KZrotqDYGKypJY0sGl82hCpZxKICgBATIXoxAf/6pMWerLP0d+BEWpoc1P83xrE9P+NShHRxTtxd0iPcCoGgysBckkKlOIz8nhhW9kTmUi3iJU0D47gn7/fsL88tomXcM+SJFCA1guSGdm2qcWis4HJwYKHgkrfEIoXPkncpfXZgInsCmS1zLsgRwoJZ6xxqV6JQckquommVvk2STZRq6SXuvmClL23LAvMBH6wae5FEqx+FpSTrcLJTcWaOHsNt6fFlmqRIj74OysO0K8usTDkdSN/XOScLzKu6EFPmUa4wPLw35b8C8ZqLtumfBv+dei1VIRjC5KHwaj1WdZ+R1vuVjFRFCR5fXeTk0kZrZMnExn3o5kdQB46bwbYmd1RSJfvajmJGdTs3iptFqXsdrje5FErjnjQOMwNXZQyGxRJlTw6XNBpR3bz5wlzQc2bgHNiQrLp5RCUyqXK+mxiHCeERaRhZtYEXb3U0fUPsT2lXt5C2Z3BwqT944x6/84mXee3efYY8ecmo5BhI5l0uNE9GYmZSbjvzuOuy5ouSGtz+aSFkhdG6JCcRYlvNQku1zRh8cNukh3jpHOqVskXkhNiY8y1EVquWGBu6NrLq7bqakqP0pSvjKKxO1tx54Q6xi2z3W4ZhZLzcm4dm7jSpMFClQq8GsUSN0Z2BUwGdAmYwKChM+6r8M40DD994Bb14xIPY8fDRBVkCpx4PXyJPVGscC5MjOm7zxE4TgyYeTCODZs6mgctpYtDEZTITivYNdJG46ulP1uagPNkQWkuNDp1L1Z7DHxs74Luup+07mxOvomNwsFdRqr0/OTPst+z3CU0j+7alaRq255aQVU1L2RmAS7QmAZcCFLpggmVtJWD0tZaqmcAmkEW44SxnmMNSSGOG4KYOtdjxEm5WslnBoniKtCxLZ0x2m3JxbqYi9Vsh6iJ5F0Z+KNg8LtYUn441/bpRLGvFs1IVxnFH0mhOTPfzzKYZY+AlTh23h8+SdfaWLIHJyiFmPpVi5lucnRVCWYrwplrlmeC4P7Yil7C9+aDPVQNwE2Kx2aMLsxaF0c/Me3616Oc4gq8PTRkVGNuGqQl0nbLeKD0dt0j0j43n9fRcGTiL07kwcA1WAitp4mI3scNgRneXW25vVtw5fRe0G0J3QtOfIE3DmIXdkHjt3kM+8fIrvPHwoUVgoCTNZBWzk7r3eBkalNIVZuj/FmfEslLI7HF2XbbuNrEKN405NQrSWZGmAK9eox5iVECbrC1WnRyaVli3DhXQr+n7jjYG+tYgSsf93jAhJB3uGIHVpuf07ikqyqOzRxBgtxvwDtevFsY84lIlQoM5xpLHoic3Hx0mrRTS+ab+bxoHzu6/Tm4aHk3C2f0zokSStKa2exss28xUxkGstv0lE+c6sc2Z16aBvWYe5ImLnEhRGLoATWTV3TEzyKZjffc2sY00a2PklHkJoUYORceIb92JaQzcmHzBU7nO0Jg1MQ57hv3INO7rIdy4uj+NE3PR5MmFgPJ3zrYsa2yW0JQYTboLoZhEFqa26ILBQsMxCRkyyeB2k9XnNCCl0bKH3b8cgtCtDDnSbMmz6KiKQSfUCBMPb0vJ/BTOwKvJ5eqcq0v9deZdy1QqHvp1ZJqKQf2O496wfKSpeOplAWU/AuZko0xKi4rz2cCfEIMnVu90xc9RDKZWxZ3+VA0F15qLRH04L2Al7fwArrVKXdNafL8wcBuOYlKc+15GZyl1l2cFAiOk5KY1xxkHhnVP6lrWm4a86k0DPoj2enN6zgychUoHMKtUACqZrDCpMEzKbsycb0dCHNgOiSGBBiWMid00sZ8mduNkySsxElQNrjHPoX/loCybpToxFxN7wB3Ld6EyioVHabZ/E2zhoNXuPKuaWn0XWecQwSl5ZIsfCiE2NG3n1YaKd31ynGxLpCAnUjtCe9BEQz1rLeqi6zpyUkLYVpW1Drf/zWLZZBlIxd6rxlQz83i8ycTVm1v/skczjKRhIEspoiCOc67GuBEmUc4kM6KckTgjsSPzSBOjKkOE3EZoIs3abNbdZk27XtH2vWkpXo2n8qkyazJH/NS/S1NX1YikTO5hr1Jm3G/Zb4cDX0bJgLSMRVfdS780V4ZRIGbLhe138thZcRjbXJiSlAEFhIw7tH1WLHFndMZkh0Fs4oyv4Sq95kUYXpol1RLZsRRgWNjJl7HX9QCof4oYY0MXXI3TBRjW1eUxTXv2uwv2E1xsI0kDY/BYaGqdGhx8eAbj0oIDX8IlJ5sqKdmwltOwFMCyO8eL1bBI5XVlPG7hKUYVcAk85XI4z+GMBwz8icLMVU3lipbqv83ZErayQPbEkbBeEdY9zbqhWbUV6fNp6Wkq8qyAXwR6//5PqOrfEZF3Av8a+Hzgd4C/pKr3n/rO3rPsqdtl0SoRDY2p+z6pu5xI+8yoie71Mx5eDHzhoz1ng9LkxFb37PZ77l/ueHC5YztlKxIQlCzREiA8C01EvACtWAVxDbYQpiJ5WEib2Q49gsSZti4k7lIOKYbGK804xokqKY8IkyeNtG6Ts6unlJmSSd2W4QZtb+iF7WrN6vS2YxArU1aG7SXb8weG7ZATQZUXbu3QZokZYckL680JKSmnt24Rm5azhxfXDzvGrEexRP+S+JXdrLHMCL3ut3bH5Zvm0EspM263DJwxhRYaKwIg00TIiUngnMwO5ZNMXKA8CJn7Ys6cbStWSWfTE1Yt7WrF+vYtYttycnqLrl8Ru5Z21Zs5w4Go5gBPaEIB3pIr9uTHXxvu9+HOnqaR8wf3eHR+OUtceuVAEzwtP9TXB2e+Y5gY+FbrUS+zNH7AQKEyWFVqEWxjHuUo8F4WjBr8sA+B1WrNZn2CYlJuTolhmpgcLiIXE6Gn7ZfmKlZerzDuwggrw0IPx0YWyVcY9LFqZkxDrRZ0laFtt2c8vP9JtoPy2lliStCFxhEr1Yt2z/ARxQekOjsl1W3VEqz2qbXRGfhi3Dz6kGUiXShmnuVZtPAlULWjMM+HumM9lcNZD88AKSbW8ju7WP3e0uRyQFrhJLSJ5L5B2pbTl95Bf+cOfQvrldBJY/U4n5KeRgLfA1+nquditTH/o4j8LPAXgI+o6veJyAeBDwLf/dR3XnRs9h6X94KzkABiTD6pMb7dkIjBak5e7kfaHMgSLM57TAyOkOaGRZbOqjn0CAhXJDThymZ+XL2WhcWvOm6KlKaLxZ8thlpyqNlbddNqYTcepgjGUKIdBEWiV4/wGMeR/W4HOdNgcdJl4y+bakwrztmGsTjrFr1ZLKqlxFCY9dLCeOXrj42KaQ5UEK+8YARTSiQNpJCscIUXBdijbMlsUc6ZuBDlHLgIJoWNoUGjENqIrFrCqqVZ9aZZ9B1N13oyTPBAg8M5ms0VC3PEYuOx+HyhVx2QajYtYhwooYFFKnQ7mq2AGAgaKzMvtwCxkoACGuZydZY9WdYg9bkumOjSDKO1pJizNikM3NZ2cOyZ4MUJSrtVstu886EtvkijOs+xus15GW997WQvRksET/AqYzMnFV2l4lAdx8Q4JMakEBsasUSnsEj392Ohap4FiE1d07ExM4NcTWYqvpoFA19M+HwA1lDHhYljuVaKxl/t6LN/Y7ajY3VVdb7+gX3Vf/tkBm5fzyIU1DXpGuKqo131xJgtE1kC1yzLJ9LTVORR4Nxftv5Q4P3A1/r7HwJ+gU+HgdfVZE+EEkPrzFKVHAKZyIjyaDuyGxO/8X8+SRbl9umG97znBVKeeP3hOfceXbCfDE2whJuqQdsZ8H6wuGCRQIPhZWfNxNTYRposiaFsXFsTNoUSqVLXctJTsoDDg7AhfAGPs5Q1z68SQ2DVtxAi680p3foEEWHnIPrj7tLiq88fcfngdYIIt09PiG177eoIoaFtLC6+aRpTrb3CuS1EDhZGYcDUY2Rm6tdqi1embCaTD0dV9igPc+LVNDABG21pgAfpkm0aeKiJ1/PEPsC9NrAXIa826GZN6Fo2tzeErmF1a0O36YldS7/ZVAgFy46dS16LH9DBHck12uTK4VUqs8wtLpv38b4JiuiI6Fi47CJ+ty4FYzQp+gY+jGrJIqCJLILmiWksZjfmtfAEE9UsGMwaoBtTvCl2ADSuAbRNpGmbCgdb7NmGzz0f3OXIqjHf6inpWmLB9bF2XHXGGaSvpdungnY47BwQ60pYFAAB0ZI+D9FNmjXuScu9iuRs3FAclEzLu24OC+6rMWFJzDzqkm/1tSwnVef1PPt0CzOXMtsUkLlZAw2O264VHXDWXZyRM+PiXLtZwvxdO8OFuFrR9g3tesX6xbu+vk9p+xW6v2R89AgkopsTaJ/Ojfm0Vekj8CvAHwa+X1V/SUQ+R1VftnHSl0XkXU/47XcC3wlw586dww8XjLsyJSnMUeuom5faIFIv9yP7YeL3X7lHzhMvvnCX/sRgaM8udpxf7pgyjFqknjgfyrGo0JbIEcUYcs7ZCutmZcx790RTT1+w2NAgwfeqLBaAn9iFodQueZQLRcqq4+EL0hJzJDT06zWr9drTp61Y7fb8gnHYsj9/xPbhQ9omctJ3lnV4LQMPM/N2SdzKouFi8kLSqmtcvQ/z2M9vy7X3qb+8clhNGBM/z4kHaSJI4J5mAsof5D1n0577OvJKHhiDcNF0TBJZd8LqZEW36unfeYe2b1kXBt5YhIlIqMlOy/YWCbQUXFgy7zIXNh/ed6Xav2djwNWOWcEH0QKna/MrBzZe8UQejxku6JOzyExyDSDnqWYLluX+mAYFXq3JE74K/Kg70gNz+F0ohgZxqN0YrA6nO51rVqWDVMXidGdemwUd8UmmsuuYtyutlj2KWuWnUtptMm3x6pWsaEis4FWGL+aHmYZ53VWpdrGi1AULb3so6eqYBqCLkEH7RTmwlxdaOBkrOymmx1mDLiJMRiqeCsFj/oWDfVBCKec3Zp21jpgLB+prTb3+Zlh3tCcrVrdOuPvuF02jpEEIjMMl28tLgjTkPh34uN6MnoqBqxmdvkJE7gI/JSJf/nSXB1X9AeAHAN773vcezHEIZo/uQmScRmKaPArC4yyrqaIsONuwmcz5dsfr95QpJ/p1B6I8PL9knCy5YT8pWcXDvYr90zfHAdYBNuCepRViJOZ50u3jEt9tZg4WDDyDSQCykGAXqndx7ohPbBOtll+IDW2/9oxSw7hWd0Shs822RFYoDkiUUvVqH4yzq5b2nclKWUVoHZmweLZnrULqsqt2w4W9r0pv11KJ0CkbwOK8R4EzTbyeR8YsNGlHAN6QzEUDF6FhHyM5BtqTNU0b2dw64eTWmq7vOF2buaTrOtqmBWdq4Gq2LO4vs007xIIzYgeXVHNS4wdZmBnjgjk9kdw5OR969pide2qmkWqaKGNRZUhqHLFnZNqBN2cGzj3BtQh3ZC9MemVFaTZo3JIYhFhIaQ7B7c9Wlb0UbCjl1KpWcI2p5mqE0dUoirIMShtNILVxyNNkuOFevq9Uy7l64Jtk7+YIN+mYLBFsX5Tw0spMMVMRc/sqg15knBan8fwomalLk8iVBVwD0Od+VlNW0UByXszpMsyzAFZR+HPt4VJ38E1hYxRM65cYadcrpIms7pywOt3Q9i2kTN6PDPtL0phIl+eM2y0S20Xi3FvTpxSFoqoPROQXgG8EXhGR97j0/R7g1U/lWmBgMOvVClLDNO7JE0wZJo/tjKH1mnYlgkTIHsP72oMz3rj3BpvXel554x4ShPsPH7IdEvvRKupkVfJkklHbtM44rUBwqfMYPakjhGCu9dzYJioSkEtF4rGqBauk2LJNWFjYtVUr+E+JqxWsIEEQQ9dbr1c0bcf65A6xsRISlkIf6JrIhNI2Ec0WbaEhGm5MNkyLK5GPZW4sCSZNDJOlY8dG6Netbe6Bg42rGNogFDMTVWCp0u61AvhCywhF/VRGNe/6qzoxph0bJl4Tk8TPgzI0gq56OLEoktO7p7Rdy52TDXc2J7Rty+nmxKrtrHpC25IEBm9GEmunZf2ZhBQ9cadpW0P6C15wWaKHEHY0bet1PQ1XOxYAoiev8oMY7zK2qsXJ5/3PiwFaMK7CkKsEG7zARQgewCS1OPRsr6cmf7Ec9nKIlAiSlMjjiIiQooX9Ta0lPU2T1WocHI/ckBZDtVakNJGX6HilsfKEaa4dKi47KAWr07Bne3Fmzr5psAipa0wo6mGzhq0yWhauuEa9DGdUM5nWgK7C+JnT5VGtWpAWZ6DbqO1eti+DGDLoQumww0nNL2Hvy3wqqVpMNnhS1TyfdV58HMqbcrBBLMU/+IBmP1CIQm4bwqqjf+mdxFXPrbu3Obl9yyB5Ly+Zxj0PX3+dy7NzQhqI4w76Nfmdn0EwKxF5CRidea+BbwD+HvBh4APA9/nfn37qu5Zrl4csHqif9IsxXvymnHcpK2lM7OLI+eWWEIRxmtzurRWUPvlBH0IkuFQypckr08xUY8QX5oTKwD1tXj3jc6lcit8vo0g1ucwqoLjKVtTPGILFMzcNXeuFUtUiQWJxkFWBWA82d9kQxTG2pAOpSm2jWAUXlwCjZ925uecg/UJ1wTRYMPHrJdUqh0pAC66Li2xTFAYTPtj5hhybSApC6Fpi39O0DW3f0XUtXdfSdpHWiyYX5oaUcZ5jTIrTtDJImQ/ZOk8+V0sH1fychXR7PR0cxD42zLN6rd28UFkXs+RaDDX+yUJzq4y7vL+4/3JOKZJhwe5wDJacU3WSV+jXhVRaR64IF3l+3zpSIl0Wb5UDxNedlnYVU2bNKvVHmvywWyIVPj6a1z202Jev+dkTZYeDb1DHVJUaURRDcKEMyqIs2C+2l9wsFQzywrRZj52XUGtzFkuKFKeizqvgYAkUBlUn3ee3MTC10HXmgF+ZSbAENKTBwm2n/UDaD6ATMaWaofm09DQS+HuAD7kdPAA/rqo/IyL/CfhxEfkO4HeBv/jUd3WqVcmzqWeNmyGCJlAhSjaniajZnhcDZAWEeyZVHp5tQYxpZ7Xq7QXWwHAiTAIREZNUhmG2n/rfYoMsnYwxOnSKgGQ3u1CjO0qstknZagUR9ntn4pZoIKI1nG3dmX36ZL3idLOm7Tpu3d4QY8swJYakRJSdF2Gdhj377RZVZb3egFoh2XGfGe70cCVXSzWTdCKTkKiEBvpNYzgxU2YaPTSzxAUjVfJO1darM+jRgRZXNrLfqzwJ0Rm4Mewg2IHURFLTMvVrmhg57dc0TUu76ug3K2ITWJ90NG1g3TWs++imJTOJJPEkKyA5Io2GBUxBLPgzCyXfGXpNoKp/Q9W2ahjhm3Bhi89PjF5OrNyjmJ3qoVHMW0hNzTetzRl3gRwtn7ldtf626O9wcFhYxIsdxCURRaYJSROaJtJ+j4i5NWObCW1XU+dzMZ04A0a12sKrdFk7Oh+Ky77XfAhncGXaRWAY9kz7HcOwJY17Svm9K3rDctmUwBIzCarBBUk0Zbf6KerJPDPkMu6zvXqpGlqYbRGyggS6tjftTaQ6NGsGp0er2F63+SlY3up7YtmF2Wc1C2TjODm+t0nzIFWYME1RbX5XKyRGVrdvsblzh9h2bO7cJjYt427H+StvMG13bO/dIw0j025naKCSaUSsGtkTV+fj9DRRKL8KfOU1778BfP2ncK/Hrw2uGgqiFiKXwEtiFTnR0r3zYq/Om6YhpYlxvwe0RiVYnsPSRmYx2jXov0QU+CKJ0Suui9BHwwG3GNISceIPL8kVYqDrDAWxxO0Kyn7wA6guNqMQhLZxuNGuoXfJc90bxrUMgkyZabT4WDST08Q0DgSUpmvRlBj3O7dzRvQqAy9qp2YvjgBNa5lvOWZiKEBG6slEc1JRqPZeDiTwpSRZxqEyb2dIuMQb3DwQ2mhxrk1LXvdobOjXJ6zaFV3fstmsCFFYrSKxEbpWaJtS5syuVRKJTEGdE6iL6ruUuqs67PNZaxCG+bul4s+BM49DG/ByURY8EJyJ1XvXe5SM3MKQ40K6npnKYRtNDauv64AWB2uRiGcmngozThMyTehkSJEA0o4QpDLuUohBF2tbvaJOjVFeSnYLLe86ktKuasKANE0M+x3jNNQs1OW1Hr/I/JBQBWJKHczi+F06qOfVFmE5PyKUgFcp/NznJ4bIquttLy00oDlxac4uLeu0qQwc8pVblXEp4aMWR6+oZB/C4kgvGEbKBMY3usY0zdMN67u3adqOzeaEECLTxZbh/JLh4pLL+2fkcSSo1eANosSIV6l/wnheQ8+3oIMWiFErk1CqsjdlfXssaJ1hKZvOh29hz7XriW/A+fljauXiOdhkWjFXSztPYgdGU0pxhUDXWVmv2HqlazG1zBxVBTfEHE2hagtSN2KBSi1Vexz5AU2jpaxPZp/M055p2DMNA3marCPBQ6TcHFBj26+Sb5LYBLquIYRMEINv1aSGp1ElcDf7FA0drQvTBZvHoHKLqg9SQ6QkiEvD1HGxCjqRVdtxZ7WmCQ0n/ZquaenallXf+AFoyTaxEWgEDYEpGtOdgEm1lCBFKUULDF8mHoCEFbPJQtJdjE9x3IF4TH7233C9ni5YREuYo1kWZRtn6bncExYso/KncveFdq1wVQheHJylTmYFlHJsFVVFxgGSVbrPw940vxCIySTwcbAq7XlyE0vK1d5NYd7Xkc6ntC7aXPoRgyCaaybwOOwZHXK4HPYHh+lj1xck22OWGFx6rRjzgEYKGufVsdSiKpS6rksDS3aHrqrb4N1mXn5ZmC9zMEGp9ZAxfHVUii91Xje6kL09ztzAJa5gsZTtECNtG5E20t85Ja46+k2PBEhp4Py++QkuHzxk9/DMMqrTiHj93dLjJwoVb0LPl4G7CSUlrSYUxMrEmYNiooTtmBBwlYmXZ54kMSNz1kCC5XhcTVgoi25ZlZs8Qk5WO7GztPb1ZmPREWhlWErrQo3VY0STZZahiFdVT1aC3vBGPJMsiqGqiSZ03JNzII8TeUxM+y3D9tLKMo0DmhNgONfZpUvNBVDrkIqG0LSB1bqz7NNWXK3WuXBrMaHoXLlE6zVKqNYsx9Rn1Z7MHH8dHa9crLRZDELXd3RtS9803O7XJh01rVV/CZHe8dub1lLhczAJKEtgiMaER7UsVBXHyZAZpS+GOJcoqxEnRRKWmYkv5tz8BpblKFJwEcOhRFrHQGbclAP7uV2zmlWkILovfnuwuJ1THAggi/Rs1Sohm4Y4Fx8uBYbHcbCszGmwQ37hxJxULcSyaenWe99LXpBhsqiU0p8nq+TqQk5lSQfrqXHhZhysQPN+v2W322In0byHFm7Ow7FUCFnMHJcUJkWxeqUO4uArTH1GAuK+qbLmdGmmKREkRcooGasSmMbB/AIHB7j3Shf8wp+HNAPTlbYfwAsvLmH7JVnbC2MXvMCHIF1Lf9oTuo7TF+/Sbda+ZpRpP3L26j2m3Z7x/JJ0uYWckWQhtnPAhC7m4enp+eKBi7IOpkaIWE3DpJmxFCQoTmHxA7pUsRagmqMEdQCfMgCpiYyx4Ds44HupRK7Ye6oepifkJP59QZOClgrWxiz6TmgaaBvoGgMl6mVCJJLCZFmiMRFb35S+MXKEKQgxwKbJtDGxkomOgaiJmISggcaTMFYMbOJE20zQmZEkBruvZmGfA5oCq+bxzdLEhr7tkLwmrU9NtW5HSn1HTVcZOLPtFYASfrVwvUn9ZGEOoGKchKCEiB9QZnbq2pambehjY5pLCDSxdedSRKIxRgoaXNn7IYBE1JEgy70L5KgQkSKhWUrb4sA2YwsqJEloUKslmqyIAtnMHpoyTWwqcNpuu7Xwt+WajJFbJwbneeBwXJpQilnlirRYn1fGsFQGZkZebaxaGPgCjySlCuo0eWV5nUZPhU/k0eEgNifEtmN9esLpukeziRTZsXOqqWUxj4sW2rgdCEGVNQHQuaNdU2LsG3JKtEEZVj0VBwaqT+Fkc/KYFB6blrbfkGViMzZMKYNjidldy2EafdVZ0d8ycEKRgMthUUw27mTF1rCIEDtLyjs8wGfGXaTqUg2+mLjKGBXzTZ3IpYamEJuRppY6Mx6knhEsfUvoLXJq3XS0oa3XDJLpQ0OMmabtST2ziVi1ho1GURpRehd6npaeKwO/1Qx88ea+JT1Uh8MMEVlU+oNgiDKJChBBA6qHHZ7V5vn1chNelcAfi+BQi+AoGCgxFjUdQigO0NEFrNlEkzf5SjtA1RZKjHuCjIS4I+ZHiAox2/UrhkrIvPuObdp0J5OLtC02CDmvUFVu9eFgUwYR7qzvsOlODPDo7vtcysuzClo06Tp8unjuQ7tcwNcyp/lzKS8PGC1uay62yYIlI5WplUMgixzGdrvkJAgNZm7S5W01mCSXCyYNTFNxFqe6cUV2B1JzdRoyOw9LW1JKbC8vD+bszq1TvurLvsRKvy06esCapP7zGD1Z2l18uJD858iQ2QY+M5vZPr60lQM1YakAoClqTL5I9ksz4RPaUfnTNSJf9RmUveRmipweRx8UsSIES7hmkcDJnRfpN7fIWXlpylUpme++XHlLzrmwZiwXbb32ov3VTl/2hFy5x+HTIrJcZ/J5M10lLw6SJWnR/KJrhW3jB5J/3ijTC7dqDL+WYhBVBCiHiB1QIURW69MntuMqPVcG3krmbrt/k2+8lTIxqz/PjgqTviYY+9rsqdK+NP+mrN2r8foCdFffWNKTTmaha3q65mlRhJ8/vdnMijxhVhcS0aHK/6nZDd+MurblpRfe8Rm73v/P1PZr2n79vJvxfCkAq8/evnyzjIYjHelIRzrS25iODPxIRzrSkW4oHRn4kY50pCPdUJInBfJ/Vm4m8hpwAbz+zG762aEXudl9uOnth5vfh5vefrj5fbhJ7f9DqvrS1TefKQMHEJFfVtWvfqY3/QzTTe/DTW8/3Pw+3PT2w83vw01vPxxNKEc60pGOdGPpyMCPdKQjHemG0vNg4D/wHO75maab3oeb3n64+X246e2Hm9+Hm97+Z28DP9KRjnSkI31m6GhCOdKRjnSkG0rPlIGLyDeKyMdE5OMi8sFnee9Ph0Tk80TkP4jIr4nI/xSR7/L33ykiPy8iv+l/39a51yISReS/i8jP+Oub1v67IvITIvLrPhd/4gb24W/6GvqoiPyoiKzezn0QkX8uIq+KyEcX7z2xvSLyPb6vPyYif/b5tPqQntCHv+/r6FdF5KfE6vyWz952fXgremYMXKyiz/cD3wR8KfCXReRLn9X9P02agL+lql8CfA3w17zNHwQ+oqpfBHzEX7+d6buAX1u8vmnt/8fAv1PVPwL8MawvN6YPIvK5wF8HvlpVvxwDtfk23t59+GGs9u2Srm2v74lvA77Mf/NPfL8/b/phHu/DzwNfrqp/FPgN4Hvgbd2HN6VnKYH/ceDjqvpbqjoAPwa8/xne/1MmVX1ZVf+bPz/DGMfnYu3+kH/tQ8Cffy4NfAoSkfcBfw74wcXbN6n9t4E/A/wQgKoOqvqAG9QHpwZYi0gDbIBP8jbug6r+InDvyttPau/7gR9T1b2q/jbwcWy/P1e6rg+q+nOqWnBh/zPwPn/+tuzDW9GzZOCfC/ze4vUn/L0bQSLy+VhpuV8CPkdVXwZj8sC7nmPT3or+EfC3OcQ+vEnt/0LgNeBfuBnoB0XkhBvUB1X9feAfYLVjXwYequrPcYP64PSk9t7Uvf1XgZ/15zeyD8+SgV+HEHojQmBE5BT4N8DfUNVHz7s9T0si8i3Aq6r6K8+7Lf8P1ABfBfxTVf1KDIrh7WRqeEtyW/H7gS8A3guciMi3P99WfUbpxu1tEflezET6I+Wta772tu4DPFsG/gng8xav34epkW9rEpEWY94/oqo/6W+/IiLv8c/fA7z6vNr3FvSngG8Vkd/BTFZfJyL/ipvTfrB18wlV/SV//RMYQ79JffgG4LdV9TVVHYGfBP4kN6sP8OT23qi9LSIfAL4F+Cs6x1HfqD4UepYM/L8CXyQiXyAiHeYw+PAzvP+nTGJlO34I+DVV/YeLjz4MfMCffwD46WfdtqchVf0eVX2fqn4+Nt7/XlW/nRvSfgBV/QPg90Tki/2trwf+FzeoD5jp5GtEZONr6usxf8pN6gM8ub0fBr5NRHoR+QLgi4D/8hza95YkIt8IfDfwraq6LMd0Y/pwQMuSYJ/tB/DNmOf3fwPf+yzv/Wm2909jatSvAv/DH98MvIB54X/T/77zebf1KfrytcDP+PMb1X7gK4Bf9nn4t8A7bmAf/i7w68BHgX8J9G/nPgA/itnrR0w6/Y43ay/wvb6vPwZ80/Nu/5v04eOYrbvs53/2du7DWz2OmZhHOtKRjnRD6ZiJeaQjHelIN5SODPxIRzrSkW4oHRn4kY50pCPdUDoy8CMd6UhHuqF0ZOBHOtKRjnRD6cjAj3SkIx3phtKRgR/pSEc60g2lIwM/0pGOdKQbSv8XfSK/+cNY3qIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GroundTruth:    cat  ship  ship plane\n"
          ]
        }
      ],
      "source": [
        "dataiter = iter(testloader)\nimages, labels = dataiter.next()\n\n# print images\nimshow(torchvision.utils.make_grid(images))\nprint('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, let's load back in our saved model (note: saving and re-loading the model\nwasn't necessary here, we only did it to illustrate how to do so):\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "net = Net()\nnet.load_state_dict(torch.load(PATH))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Okay, now let us see what the neural network thinks these examples above are:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "outputs = net(images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The outputs are energies for the 10 classes.\nThe higher the energy for a class, the more the network\nthinks that the image is of the particular class.\nSo, let's get the index of the highest energy:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted:    cat  ship  ship plane\n"
          ]
        }
      ],
      "source": [
        "# 1是维度，即每行最大值。返回两个值,第一个是tensor是每行最大值，第二个tensor是每行最大值索引\n",
        "_, predicted = torch.max(outputs, 1)  \n",
        "\n",
        "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
        "                              for j in range(4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The results seem pretty good.\n\nLet us look at how the network performs on the whole dataset.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 55 %\n"
          ]
        }
      ],
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
        "    100 * correct / total))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "That looks way better than chance, which is 10% accuracy (randomly picking\na class out of 10 classes).\nSeems like the network learnt something.\n\nHmmm, what are the classes that performed well, and the classes that did\nnot perform well:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of plane : 70 %\nAccuracy of   car : 57 %\nAccuracy of  bird : 30 %\nAccuracy of   cat : 22 %\nAccuracy of  deer : 32 %\nAccuracy of   dog : 57 %\nAccuracy of  frog : 83 %\nAccuracy of horse : 62 %\nAccuracy of  ship : 65 %\nAccuracy of truck : 77 %\n"
          ]
        }
      ],
      "source": [
        "class_correct = list(0. for i in range(10))\nclass_total = list(0. for i in range(10))\nwith torch.no_grad():\n    for data in testloader:\n        images, labels = data\n        outputs = net(images)\n        _, predicted = torch.max(outputs, 1)\n        c = (predicted == labels).squeeze()\n        for i in range(4):\n            label = labels[i]\n            class_correct[label] += c[i].item()\n            class_total[label] += 1\n\n\nfor i in range(10):\n    print('Accuracy of %5s : %2d %%' % (\n        classes[i], 100 * class_correct[i] / class_total[i]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Okay, so what next?\n\nHow do we run these neural networks on the GPU?\n\nTraining on GPU\n----------------\nJust like how you transfer a Tensor onto the GPU, you transfer the neural\nnet onto the GPU.\n\nLet's first define our device as the first visible cuda device if we have\nCUDA available:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n# Assuming that we are on a CUDA machine, this should print a CUDA device:\n\nprint(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The rest of this section assumes that ``device`` is a CUDA device.\n\nThen these methods will recursively go over all modules and convert their\nparameters and buffers to CUDA tensors:\n\n.. code:: python\n\n    net.to(device)\n\n\nRemember that you will have to send the inputs and targets at every step\nto the GPU too:\n\n.. code:: python\n\n        inputs, labels = data[0].to(device), data[1].to(device)\n\nWhy dont I notice MASSIVE speedup compared to CPU? Because your network\nis really small.\n\n**Exercise:** Try increasing the width of your network (argument 2 of\nthe first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\nthey need to be the same number), see what kind of speedup you get.\n\n**Goals achieved**:\n\n- Understanding PyTorch's Tensor library and neural networks at a high level.\n- Train a small neural network to classify images\n\nTraining on multiple GPUs\n-------------------------\nIf you want to see even more MASSIVE speedup using all of your GPUs,\nplease check out :doc:`data_parallel_tutorial`.\n\nWhere do I go next?\n-------------------\n\n-  :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n-  `Train a state-of-the-art ResNet network on imagenet`_\n-  `Train a face generator using Generative Adversarial Networks`_\n-  `Train a word-level language model using Recurrent LSTM networks`_\n-  `More examples`_\n-  `More tutorials`_\n-  `Discuss PyTorch on the Forums`_\n-  `Chat with other users on Slack`_\n\n\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
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